Index (bayesserver 11.0 API)

A B C D E F G H I J K L M N O P Q R S T U V W Z
All Classes All Packages

A

A_TO_B - com.bayesserver.learning.structure.LinkConstraintMethod: Enforces a link from A to B.
A_TO_B_IF_CHOICE - com.bayesserver.learning.structure.LinkConstraintMethod: If a link is detected between A and B, direct it from A to B if a choice of direction is available.
A_TO_B_IF_EXISTS - com.bayesserver.learning.structure.LinkConstraintMethod: If a link is detected between A and B, ensure it is directed from A to B.
A_TO_B_OR_B_TO_A - com.bayesserver.learning.structure.LinkConstraintMethod: Enforces a link between A and B, in either direction.
Abduction - Class in com.bayesserver.causal: Performs abduction which is one of the steps in 'counterfactual analysis'.
AbductionOptions - Class in com.bayesserver.causal: Options for Abduction.update(com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, java.util.List<com.bayesserver.Variable>, com.bayesserver.causal.AbductionOptions).
AbductionOptions() - Constructor for class com.bayesserver.causal.AbductionOptions
adapt(Evidence, OnlineLearningOptions) - Method in class com.bayesserver.learning.parameters.OnlineLearning: Adapt the parameters of a Bayesian network using Bayesian statistics.
add(int, CustomProperty) - Method in class com.bayesserver.CustomPropertyCollection
add(int, DataColumn) - Method in class com.bayesserver.data.DataColumnCollection: Adds a DataColumn instance at the given index.
add(int, DataRow) - Method in class com.bayesserver.data.DataRowCollection: Adds a DataRow instance at the given index.
add(int, QueryDistribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
add(int, QueryFunction) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
add(int, LinkConstraint) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
add(int, Link) - Method in class com.bayesserver.NetworkLinkCollection: Inserts an element into the collection at the specified index.
add(int, Node) - Method in class com.bayesserver.NetworkNodeCollection: Inserts an element into the collection at the specified index.
add(int, NodeGroup) - Method in class com.bayesserver.NetworkNodeGroupCollection
add(int, State) - Method in class com.bayesserver.StateCollection: Inserts an element into the collection at the specified index.
add(int, Variable) - Method in class com.bayesserver.data.sampling.ExcludedVariables
add(int, Variable) - Method in class com.bayesserver.NodeVariableCollection: Inserts an element into the collection at the specified index.
add(int, String) - Method in class com.bayesserver.NodeGroupCollection: Inserts an element into the collection at the specified index.
add(Distribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection: Adds the specified distribution, automatically creating a QueryDistribution instance.
add(Distribution) - Method in interface com.bayesserver.inference.QueryDistributionCollection: Adds the specified distribution, automatically creating a QueryDistribution instance.
add(QueryFunctionOutput) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection: Adds the specified function, automatically creating a QueryFunction instance.
add(QueryFunctionOutput) - Method in interface com.bayesserver.inference.QueryFunctionCollection: Adds the specified function output, automatically creating a QueryFunction instance.
add(Table) - Method in class com.bayesserver.Table: Adds the values from another table into this instance.
add(Object...) - Method in class com.bayesserver.data.DataRowCollection: Adds a new row of values to the collection.
add(String, Class) - Method in class com.bayesserver.data.DataColumnCollection: Adds a new DataColumn to the collection.
addAll(double) - Method in class com.bayesserver.Table: Adds the specified value onto all table elements.
addMonitor(NetworkMonitor) - Method in class com.bayesserver.Network: For internal use only.
AdjustmentNotFoundException - Exception in com.bayesserver.causal: Raised by a causal inference algorithm when an adjustment set cannot be found.
AdjustmentNotFoundException() - Constructor for exception com.bayesserver.causal.AdjustmentNotFoundException: Initializes a new instance of the AdjustmentNotFoundException class.
AdjustmentNotFoundException(String) - Constructor for exception com.bayesserver.causal.AdjustmentNotFoundException: Initializes a new instance of the AdjustmentNotFoundException class with a specified error message.
AdjustmentNotFoundException(String, Throwable) - Constructor for exception com.bayesserver.causal.AdjustmentNotFoundException: Initializes a new instance of the AdjustmentNotFoundException class with a specified error message and a reference to the inner exception that is the cause of this exception.
AdjustmentNotFoundException(Throwable) - Constructor for exception com.bayesserver.causal.AdjustmentNotFoundException: Initializes a new instance of the AdjustmentNotFoundException class with a reference to the inner exception that is the cause of this exception.
AdjustmentSet - Class in com.bayesserver.causal: The set of nodes that an estimation procedure must adjust for (condition on) to avoid any bias in the results.
AdjustmentSet(AdjustmentSetNode...) - Constructor for class com.bayesserver.causal.AdjustmentSet: Initializes a new instance of the AdjustmentSet class.
AdjustmentSet(List<AdjustmentSetNode>) - Constructor for class com.bayesserver.causal.AdjustmentSet: Initializes a new instance of the AdjustmentSet class.
AdjustmentSetNode - Class in com.bayesserver.causal: Represents a node in an adjustment set.
AdjustmentSetNode(Node) - Constructor for class com.bayesserver.causal.AdjustmentSetNode: Initializes a new instance of the AdjustmentSetNode class.
AdjustmentSetNode(Node, Integer) - Constructor for class com.bayesserver.causal.AdjustmentSetNode: Initializes a new instance of the AdjustmentSetNode class.
ALL - com.bayesserver.learning.parameters.DistributionMonitoring: Monitor all distributions.
ALL_EVIDENCE - com.bayesserver.inference.InconsistentEvidenceMode: In this mode, all evidence is checked for inconsistencies, by checking the log-likelihood, and raising an exception if -Infinity.
ArcReversal - Class in com.bayesserver: Contains methods to reverse the direction of a Link, known as arc reversal.
areAllValuesNonZero() - Method in class com.bayesserver.Table: Returns true if none of the values in the Table equal zero, or false otherwise.
ASCENDING - com.bayesserver.data.discovery.SortOrder: Discrete states should be sorted in ascending order.
ASCENDING - com.bayesserver.NoisyOrder: The states of the parent affect the noisy node states in increasing order.
ASCENDING_FREQUENCY - com.bayesserver.data.discovery.SortOrder: Discrete states should be sorted in order of ascending frequency of ocuurence in the data.
AssignedDefinition - Class in com.bayesserver.inference: Identifies the node that is assigned to a clique in a Junction Tree.
Association - Class in com.bayesserver.analysis: Calculates the strength between pairs of variables or sets of variables.
AssociationOptions - Class in com.bayesserver.analysis: Options that affect the link strength algorithm.
AssociationOptions() - Constructor for class com.bayesserver.analysis.AssociationOptions
AssociationOutput - Class in com.bayesserver.analysis: Contains the results of an Association analysis.
AssociationPair - Class in com.bayesserver.analysis: Defines two sets of variables to be analyzed by the Association algorithm.
AssociationPair(Node, Node) - Constructor for class com.bayesserver.analysis.AssociationPair: Initializes a new instance of the AssociationPair class with individual nodes.
AssociationPair(Variable, Variable) - Constructor for class com.bayesserver.analysis.AssociationPair: Initializes a new instance of the AssociationPair class with individual variables.
AssociationPair(List<VariableContext>, List<VariableContext>) - Constructor for class com.bayesserver.analysis.AssociationPair: Initializes a new instance of the AssociationPair class with two sets of variable contexts.
AssociationPairOutput - Class in com.bayesserver.analysis: Contains the results of the association calculations between two sets of variables.
AUTO - com.bayesserver.learning.parameters.InitializationMethod: Automatically select the best algorithm.
AutoInsight - Class in com.bayesserver.analysis: Uses comparison queries to automatically derive insight about a target variable from a trained network.
AutoInsightJSDivergence - Enum in com.bayesserver.analysis: Determines the type of Jensen Shannon divergence calculations, if any, performed during an auto insight analysis.
AutoInsightKLDivergence - Enum in com.bayesserver.analysis: Determines the type of KL divergence calculations, if any, performed during an auto insight analysis.
AutoInsightOptions - Class in com.bayesserver.analysis: Options that affect auto-insight calculations.
AutoInsightOptions() - Constructor for class com.bayesserver.analysis.AutoInsightOptions
AutoInsightOutput - Class in com.bayesserver.analysis: Contains the results obtained from AutoInsight.
AutoInsightSamplingOptions - Class in com.bayesserver.analysis: Options that affect any sampling required during auto-insight calculations.
AutoInsightSamplingOptions() - Constructor for class com.bayesserver.analysis.AutoInsightSamplingOptions
AutoInsightStateOutput - Class in com.bayesserver.analysis: Contains the results obtained from AutoInsight for each test variable.
AutoInsightStateOutputCollection - Class in com.bayesserver.analysis: Represents a collection of AutoInsightStateOutput instances.
AutoInsightVariableOutput - Class in com.bayesserver.analysis: Represents the output obtained from AutoInsight for a test variable.
AutoInsightVariableOutputCollection - Class in com.bayesserver.analysis: Represents a collection of AutoInsightVariableOutput instances.

B

BackdoorCriterion - Class in com.bayesserver.causal: Uses the 'Backdoor Criterion' to identify 'adjustment sets', that if found can be used to estimate the causal effect using theBackdoorInference.
BackdoorCriterion(Network) - Constructor for class com.bayesserver.causal.BackdoorCriterion: Initializes a new instance of the BackdoorCriterion class.
BackdoorCriterionOptions - Class in com.bayesserver.causal: Options for BackdoorCriterion.
BackdoorCriterionOptions() - Constructor for class com.bayesserver.causal.BackdoorCriterionOptions
BackdoorCriterionOutput - Class in com.bayesserver.causal: The output from the Backdoor criterion, including any 'adjustment sets' identified.
BackdoorGraph - Class in com.bayesserver.causal: Methods for constructing the Backdoor graph or proper Backdoor graph from a Bayesian network.
BackdoorGraphOptions - Class in com.bayesserver.causal: Options for 'Backdoor graph' construction.
BackdoorGraphOptions() - Constructor for class com.bayesserver.causal.BackdoorGraphOptions
BackdoorInference - Class in com.bayesserver.causal: Estimates the causal effect, using the 'Backdoor Adjustment' formula to avoid confounding bias.
BackdoorInference(Network) - Constructor for class com.bayesserver.causal.BackdoorInference: Initializes a new instance of the BackdoorInference class.
BackdoorInferenceFactory - Class in com.bayesserver.causal: Uses the factory design pattern to create inference related objects for the Backdoor adjustment algorithm.
BackdoorInferenceFactory() - Constructor for class com.bayesserver.causal.BackdoorInferenceFactory
BackdoorMethod - Enum in com.bayesserver.causal: The sets for the Backdoor criterion to find.
BackdoorQueryOptions - Class in com.bayesserver.causal: Options for BackdoorInference
BackdoorQueryOptions() - Constructor for class com.bayesserver.causal.BackdoorQueryOptions: Initializes a new instance of the BackdoorQueryOptions class.
BackdoorQueryOutput - Class in com.bayesserver.causal: Returns any information, in addition to the distributions, that is requested from a query.
BackdoorQueryOutput() - Constructor for class com.bayesserver.causal.BackdoorQueryOutput: Initializes a new instance of the BackdoorQueryOutput class.
BackdoorValidationOptions - Class in com.bayesserver.causal: Options for Backdoor Criterion validation, which can be used to test whether adjustment sets are valid.
BackdoorValidationOptions(AdjustmentSet) - Constructor for class com.bayesserver.causal.BackdoorValidationOptions: Initializes a new instance of the BackdoorValidationOptions class.
begin(QueryLifecycleBegin) - Method in interface com.bayesserver.inference.QueryLifecycle: Called before the query is computed.
beginUpdate() - Method in class com.bayesserver.inference.DefaultEvidence: Disables change notifications (if present), until Evidence.endUpdate() is called.
beginUpdate() - Method in interface com.bayesserver.inference.Evidence: Disables change notifications (if present), until Evidence.endUpdate() is called.
BOOL - com.bayesserver.ExpressionReturnType: Expression returns a boolean value.
BOOLEAN - com.bayesserver.StateValueType: A State can have a boolean value.
Bounds - Class in com.bayesserver: Stores the position and size of an element.
Bounds(double, double, double, double) - Constructor for class com.bayesserver.Bounds: Initializes a new instance of the Bounds class.

C

calculate(double, double) - Static method in class com.bayesserver.statistics.IntervalStatistics: Calculates statistics for a single interval.
calculate(CLGaussian, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy: Measures the uncertainty of a distribution.
calculate(CLGaussian, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy: Measures the uncertainty of a distribution conditional on one or more variables.
calculate(Distribution, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy: Measures the uncertainty of a distribution.
calculate(Distribution, VariableContext, VariableContext, LogarithmBase) - Static method in class com.bayesserver.statistics.MutualInformation: Measures the dependence between two variables.
calculate(Distribution, VariableContext, VariableContext, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.MutualInformation: Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
calculate(Distribution, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy: Measures the uncertainty of a distribution conditional on one or more variables.
calculate(Distribution, List<VariableContext>, List<VariableContext>, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.MutualInformation: Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
calculate(Network, Distribution, Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact: Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
calculate(Network, Distribution, StateContext[], Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact: Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
calculate(Network, Evidence, List<Variable>, LogLikelihoodAnalysisOptions) - Static method in class com.bayesserver.analysis.LogLikelihoodAnalysis: Analyzes the log-likelihood based on subsets of evidence.
calculate(Network, Variable, Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact: Analyzes the impact of sets of evidence on a hypothesis state and its variable.
calculate(Network, Variable, State, Evidence, List<Variable>, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact: Analyzes the impact of sets of evidence on a hypothesis state and its variable.
calculate(Network, List<Node>, List<Node>, Evidence, DSeparationOptions) - Static method in class com.bayesserver.analysis.DSeparation: Calculates whether sets of nodes are D-Separated, given any evidence.
calculate(Network, List<Node>, List<Integer>, List<Node>, List<Integer>, Evidence, DSeparationOptions) - Static method in class com.bayesserver.analysis.DSeparation: Calculates whether sets of nodes are D-Separated, given any evidence, and associated times for any temporal nodes.
calculate(State, List<Variable>, Evidence, AutoInsightOptions) - Static method in class com.bayesserver.analysis.AutoInsight: Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(State, List<Variable>, InferenceFactory) - Static method in class com.bayesserver.analysis.AutoInsight: Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(State, List<Variable>, InferenceFactory, Evidence) - Static method in class com.bayesserver.analysis.AutoInsight: Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(Table) - Static method in class com.bayesserver.statistics.IntervalStatistics: Calculates statistics using table probabilities as weights for each interval.
calculate(Table, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy: Measures the uncertainty of a distribution.
calculate(Table, List<VariableContext>, LogarithmBase) - Static method in class com.bayesserver.statistics.Entropy: Measures the uncertainty of a distribution conditional on one or more variables.
calculate(VariableContext, List<VariableContext>, Evidence, InferenceFactory, ValueOfInformationOptions) - Static method in class com.bayesserver.analysis.ValueOfInformation: Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
calculate(Variable, Variable, CausalEffectKind, Evidence, InferenceFactory, EffectsAnalysisOptions) - Static method in class com.bayesserver.causal.EffectsAnalysis: Calculate the causal effect on a target, varying for different treatment values.
calculate(Variable, List<Interval<Double>>, List<Variable>, Evidence, AutoInsightOptions) - Static method in class com.bayesserver.analysis.AutoInsight: Uses comparison queries to automatically derive insight about a target variable from a trained network.
calculate(Variable, List<Variable>, Evidence, InferenceFactory, ValueOfInformationOptions) - Static method in class com.bayesserver.analysis.ValueOfInformation: Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
calculate(List<AssociationPair>, Evidence, AssociationOptions) - Static method in class com.bayesserver.analysis.Association: Calculates the association/information between two sets of variables, such as those at either end of a Link.
calculateStreamed(Network, Distribution, Evidence, List<Variable>, ImpactAction, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact: Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
calculateStreamed(Network, Distribution, StateContext[], Evidence, List<Variable>, ImpactAction, ImpactOptions) - Static method in class com.bayesserver.analysis.Impact: Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
calculateStreamed(Network, Evidence, List<Variable>, LogLikelihoodAnalysisAction, LogLikelihoodAnalysisOptions) - Static method in class com.bayesserver.analysis.LogLikelihoodAnalysis: Analyzes the log-likelihood based on subsets of evidence.
Cancellation - Interface in com.bayesserver: Interface for cancelling long running operations.
canUpdate(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions: Determines whether the distribution at the specified temporal order can be updated.
canUpdate(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions: Determines whether the distribution at the specified temporal order can be updated.
CausalEffectKind - Enum in com.bayesserver.inference: The type of causal effect to identify or estimate.
CausalInferenceBase - Class in com.bayesserver.causal: Base class for Causal inference engines used by internal algorithms.
CausalInferenceBase(Network, InferenceFactory) - Constructor for class com.bayesserver.causal.CausalInferenceBase: Initializes a new instance of the CausalInferenceBase class.
CausalNode - Class in com.bayesserver.causal: Represents a reference to any node in a Causal model, for example a treatment (X), an outcome (Y), an unobserved node (U).
CausalNode(Node) - Constructor for class com.bayesserver.causal.CausalNode: Initializes a new instance of the CausalNode class.
CausalNode(Node, Integer) - Constructor for class com.bayesserver.causal.CausalNode: Initializes a new instance of the CausalNode class.
CausalObservability - Enum in com.bayesserver: Gets or sets the observability of a node which is causal.
causalObservabilityChanged(Node, CausalObservability, CausalObservability) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
CausalQueryOptionsBase - Class in com.bayesserver.causal: Base class for causal query options.
CausalQueryOptionsBase() - Constructor for class com.bayesserver.causal.CausalQueryOptionsBase
CausalQueryOutput - Interface in com.bayesserver.causal: Additional outputs specific to causal queries.
CausalQueryOutputBase - Class in com.bayesserver.causal: Base class for causal algorithm output.
CausalQueryOutputBase() - Constructor for class com.bayesserver.causal.CausalQueryOutputBase
cdf(double) - Method in interface com.bayesserver.analysis.EmpiricalDensity: Calculates an approximate value for the cumulative distribution function (cdf).
cdf(double) - Method in class com.bayesserver.analysis.HistogramDensity: Calculates an approximate value for cdf(x).
ChowLiuLinkOutput - Class in com.bayesserver.learning.structure: Contains information about a new link learnt using the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning algorithm.
ChowLiuStructuralLearning - Class in com.bayesserver.learning.structure: A structural learning algorithm for Bayesian networks based on the Chow-Liu algorithm.
ChowLiuStructuralLearning() - Constructor for class com.bayesserver.learning.structure.ChowLiuStructuralLearning
ChowLiuStructuralLearningOptions - Class in com.bayesserver.learning.structure: Options for structural learning with the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning class.
ChowLiuStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
ChowLiuStructuralLearningOutput - Class in com.bayesserver.learning.structure: Contains information returned from the com.bayesserver.learning.structure.chowliu.ChowLiuStructuralLearning algorithm.
ChowLiuStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure: Progress information returned from the Chow-Liu structural learning algorithm.
clear() - Method in class com.bayesserver.CustomPropertyCollection
clear() - Method in class com.bayesserver.data.DataColumnCollection: Removes all columns for the collection.
clear() - Method in class com.bayesserver.data.DataRowCollection: Removes all the rows from the collection.
clear() - Method in class com.bayesserver.data.sampling.ExcludedVariables
clear() - Method in class com.bayesserver.inference.DefaultEvidence: Clears any evidence on all variables.
clear() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
clear() - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
clear() - Method in interface com.bayesserver.inference.Evidence: Clears any evidence on all variables, and resets the Evidence.getWeight() to 1.
clear() - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
clear() - Method in class com.bayesserver.NetworkLinkCollection: Removes all elements from the collection.
clear() - Method in class com.bayesserver.NetworkNodeCollection: Removes all elements from the collection.
clear() - Method in class com.bayesserver.NetworkNodeGroupCollection
clear() - Method in class com.bayesserver.NodeGroupCollection: Removes all elements from the collection.
clear() - Method in class com.bayesserver.NodeVariableCollection: Removes all elements from the collection.
clear() - Method in class com.bayesserver.StateCollection
clear(Node) - Method in class com.bayesserver.inference.DefaultEvidence: Clears evidence on a node's variables.
clear(Node) - Method in interface com.bayesserver.inference.Evidence: Clears evidence on a node's variables.
clear(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Clears evidence on a node's single variable.
clear(Node, Integer) - Method in interface com.bayesserver.inference.Evidence: Clears evidence on a node's single variable.
clear(Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Clears any evidence on a variable.
clear(Variable) - Method in interface com.bayesserver.inference.Evidence: Clears evidence on a variable.
clear(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Clears evidence on a variable at the specified time.
clear(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Clears evidence on a variable at the specified time.
CLEAR - com.bayesserver.CollectionAction: Specifies that the entire collection has been cleared.
CLGaussian - Class in com.bayesserver: Represents a Conditional Linear Gaussian probability distribution.
CLGaussian(CLGaussian) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class, copying the source distribution.
CLGaussian(CLGaussian, Integer) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class, copying the source distribution but shifting any times by the specified number of units.
CLGaussian(Node) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the variables of a single node.
CLGaussian(Node, Integer) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the variables of a single node at the specified time.
CLGaussian(Variable) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with a single variable.
CLGaussian(Variable[]) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the specified variables.
CLGaussian(VariableContext) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class from a single VariableContext.
CLGaussian(VariableContext[]) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(VariableContext[], int) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(VariableContext[], int, HeadTail) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(Variable, Integer) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with a single variable at the specified time.
CLGaussian(List<Variable>, Integer) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the specified variables at a particular time.
CLGaussian(List<Variable>, Integer, HeadTail) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the specified variables.
CLGaussian(List<VariableContext>) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts].
CLGaussian(List<VariableContext>, HeadTail) - Constructor for class com.bayesserver.CLGaussian: Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts].
CliqueDefinition - Class in com.bayesserver.inference: The definition of a clique in a junction tree, without the instantiation of the distribution.
close() - Method in interface com.bayesserver.data.DataReader: Close the reader and any associated resources, such as database connections or files.
close() - Method in class com.bayesserver.data.DataReaderFiltered
close() - Method in class com.bayesserver.data.DataTableReader
close() - Method in class com.bayesserver.data.DefaultEvidenceReader: Closes any resources associated with the data such as database connections, files etc...
close() - Method in interface com.bayesserver.data.EvidenceReader: Closes any resources associated with the data such as database connections, files etc...
close() - Method in class com.bayesserver.data.timeseries.WindowDataReader: Close the reader and any associated resources, such as database connections or files.
CLOSED - com.bayesserver.IntervalEndPoint: The end point of an interval is closed.
ClusterCount - Class in com.bayesserver.analysis: Methods to determine the number of clusters (discrete states of a latent variable).
ClusterCountActions - Interface in com.bayesserver.analysis: Actions which the caller must implement to use ClusterCount.
ClusterCountOptions - Class in com.bayesserver.analysis: Options used by ClusterCount.
ClusterCountOptions() - Constructor for class com.bayesserver.analysis.ClusterCountOptions
ClusterCountOutput - Class in com.bayesserver.analysis: Output information returned from ClusterCount.
Clustering - Class in com.bayesserver.data.discovery: Discretizes continuous data in bins, using a probabilistic clustering algorithm.
Clustering() - Constructor for class com.bayesserver.data.discovery.Clustering
CLUSTERING - com.bayesserver.data.discovery.DiscretizationMethod: Discretize using a probabilistic clustering algorithm.
CLUSTERING - com.bayesserver.learning.parameters.InitializationMethod: An iterative clustering algorithm is used, which picks cases that are different from each other.
ClusteringLinkOutput - Class in com.bayesserver.learning.structure: Contains information about a new link learnt using the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning algorithm.
ClusteringStructuralLearning - Class in com.bayesserver.learning.structure: A structural learning algorithm for a cluster model (a.k.a mixture model).
ClusteringStructuralLearning() - Constructor for class com.bayesserver.learning.structure.ClusteringStructuralLearning
ClusteringStructuralLearningOptions - Class in com.bayesserver.learning.structure: Options for structural learning with the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning class.
ClusteringStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
ClusteringStructuralLearningOutput - Class in com.bayesserver.learning.structure: Contains information returned from the com.bayesserver.learning.structure.clustering.ClusteringStructuralLearning algorithm.
ClusteringStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure: Progress information returned from the Clustering structural learning algorithm.
ClusterScore - Class in com.bayesserver.analysis: Contains the results of a cluster configuration returned from ClusterCount.
CollectionAction - Enum in com.bayesserver: Specifies how the collection is changed.
ColumnValueType - Enum in com.bayesserver.data: Specifies the type of data in a column.
com.bayesserver - package com.bayesserver
com.bayesserver.analysis - package com.bayesserver.analysis
com.bayesserver.causal - package com.bayesserver.causal
com.bayesserver.data - package com.bayesserver.data
com.bayesserver.data.discovery - package com.bayesserver.data.discovery
com.bayesserver.data.distributed - package com.bayesserver.data.distributed
com.bayesserver.data.sampling - package com.bayesserver.data.sampling
com.bayesserver.data.timeseries - package com.bayesserver.data.timeseries
com.bayesserver.inference - package com.bayesserver.inference
com.bayesserver.learning.parameters - package com.bayesserver.learning.parameters
com.bayesserver.learning.structure - package com.bayesserver.learning.structure
com.bayesserver.optimization - package com.bayesserver.optimization
com.bayesserver.statistics - package com.bayesserver.statistics
CombinationAction - Interface in com.bayesserver.analysis: Interface to receive combinations from the Combinations.enumerate(java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.CombinationAction, com.bayesserver.analysis.CombinationOptions) method.
CombinationOptions - Class in com.bayesserver.analysis: Determines which combinations are generated by Combinations.
CombinationOptions() - Constructor for class com.bayesserver.analysis.CombinationOptions
Combinations - Class in com.bayesserver.analysis: Generates the available state combinations for a set of variables or counts.
combine(int, CrossValidationTestResult[]) - Method in interface com.bayesserver.data.CrossValidationActions: A user supplied function to combine the test results over multiple partitioning.
combine(Iterable<CrossValidationTestResult>, CrossValidationCombineMethod) - Static method in class com.bayesserver.data.CrossValidation: Provides standard ways of combining numeric test results from a number of partitions.
compareTo(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributionKey
compareTo(Variable) - Method in class com.bayesserver.Variable
compareTo(T) - Method in class com.bayesserver.Interval
ConfusionMatrix - Class in com.bayesserver.analysis: Calculates a confusion matrix for a network which is used to predict discrete values (classification).
ConfusionMatrixCell - Class in com.bayesserver.analysis: Contains statistics about a cell in a ConfusionMatrix.
ConfusionMatrixCell() - Constructor for class com.bayesserver.analysis.ConfusionMatrixCell
ConstraintNotSatisfiedException - Exception in com.bayesserver.analysis: Exception raised when parameter tuning attempts to solve for a constraint that cannot be satisfied by the change(s) in parameters.
ConstraintNotSatisfiedException() - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException: Initializes a new instance of the ConstraintNotSatisfiedException class.
ConstraintNotSatisfiedException(String) - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException: Initializes a new instance of the ConstraintNotSatisfiedException class.
ConstraintNotSatisfiedException(String, Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException: Initializes a new instance of the ConstraintNotSatisfiedException class with a specified error message and a reference to the inner exception that is the cause of this exception.
ConstraintNotSatisfiedException(Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintNotSatisfiedException: Initializes a new instance of the ConstraintNotSatisfiedException class a reference to the inner exception that is the cause of this exception.
ConstraintSatisfiedException - Exception in com.bayesserver.analysis: Exception raised when parameter tuning attempts to solve for a constraint that is already true.
ConstraintSatisfiedException() - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException: Initializes a new instance of the ConstraintSatisfiedException class.
ConstraintSatisfiedException(String) - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException: Initializes a new instance of the ConstraintSatisfiedException class.
ConstraintSatisfiedException(String, Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException: Initializes a new instance of the ConstraintSatisfiedException class with a specified error message and a reference to the inner exception that is the cause of this exception.
ConstraintSatisfiedException(Throwable) - Constructor for exception com.bayesserver.analysis.ConstraintSatisfiedException: Initializes a new instance of the ConstraintSatisfiedException class a reference to the inner exception that is the cause of this exception.
contains(Variable) - Method in class com.bayesserver.VariableContextCollection: Determines whether a Variable is in the collection.
contains(VariableContext, boolean) - Method in class com.bayesserver.VariableContextCollection: Determines whether a variable-time (and optionally Head/Tail) combination is contained in the collection.
contains(Variable, Integer) - Method in class com.bayesserver.VariableContextCollection: Determines whether a Variable is in the collection at the specified [time].
contains(Object) - Method in class com.bayesserver.data.sampling.ExcludedVariables: Determines whether the specified variable is excluded.
contains(Object) - Method in class com.bayesserver.NetworkLinkCollection: Determines whether a Link is in the collection.
contains(Object) - Method in class com.bayesserver.NetworkNodeCollection: Determines whether a Node is in the collection.
contains(Object) - Method in class com.bayesserver.NetworkVariableCollection: Determines whether a Variable is in the collection.
contains(Object) - Method in class com.bayesserver.NodeGroupCollection: Determines whether a group name is in the collection.
contains(Object) - Method in class com.bayesserver.NodeVariableCollection: Determines whether a Variable is in the collection.
contains(Object) - Method in class com.bayesserver.StateCollection: Determines whether a State is in the collection.
contains(String) - Method in interface com.bayesserver.NameValuesReader: Determines whether a value exists for a particular name.
contains(T) - Method in class com.bayesserver.Interval: Determines whether a value is within this interval.
containsAll(VariableContextCollection, boolean) - Method in class com.bayesserver.VariableContextCollection: Determines whether all [items] are matched in the collection.
containsAll(List<Variable>) - Method in class com.bayesserver.VariableContextCollection: Determines whether all [items] are matched in the collection.
containsAll(List<Variable>, List<Integer>) - Method in class com.bayesserver.VariableContextCollection: Determines whether all [items] are matched in the collection.
containsAll(List<VariableContext>, boolean) - Method in class com.bayesserver.VariableContextCollection: Determines whether all [items] are matched in the collection at the specified times.
containsAny(VariableContextCollection, boolean) - Method in class com.bayesserver.VariableContextCollection: Determines whether any [items] are matched in the collection.
containsAny(List<Variable>, List<Integer>) - Method in class com.bayesserver.VariableContextCollection: Determines whether any [items] are matched in the collection.
CONTEMPORAL - com.bayesserver.TemporalType: A standard node that is not repeated at each time step.
CONTINUOUS - com.bayesserver.VariableValueType: Continuous data.
ConvergenceException - Exception in com.bayesserver.inference: Exception raised when an iterative inference algorithm fails to converge to within a given tolerance.
ConvergenceException() - Constructor for exception com.bayesserver.inference.ConvergenceException: Initializes a new instance of the ConvergenceException class.
ConvergenceException(String) - Constructor for exception com.bayesserver.inference.ConvergenceException: Initializes a new instance of the ConvergenceException class.
ConvergenceException(String, Exception) - Constructor for exception com.bayesserver.inference.ConvergenceException: Initializes a new instance of the ConvergenceException class.
ConvergenceMethod - Enum in com.bayesserver.learning.parameters: The method used to determine whether learning has converged.
convert(Network, Evidence, Distribution, BackdoorGraphOptions) - Static method in class com.bayesserver.causal.BackdoorGraph: Constructs the Backdoor graph or the proper Backdoor graph from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
convert(Network, Evidence, Distribution, IndirectGraphOptions) - Static method in class com.bayesserver.causal.IndirectGraph: Constructs the 'Indirect graph' from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
convert(Network, List<CausalNode>, List<CausalNode>, BackdoorGraphOptions) - Static method in class com.bayesserver.causal.BackdoorGraph: Constructs the Backdoor graph or the proper Backdoor graph from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
convert(Network, List<CausalNode>, List<CausalNode>, IndirectGraphOptions) - Static method in class com.bayesserver.causal.IndirectGraph: Constructs the 'Indirect graph' from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
copy() - Method in class com.bayesserver.CLGaussian: Creates a copy of the distribution.
copy() - Method in class com.bayesserver.CustomProperty: Makes a copy of this instance.
copy() - Method in class com.bayesserver.data.DataColumn: Copies the DataColumn instance.
copy() - Method in class com.bayesserver.data.DataRow: Creates a copy of this instance.
copy() - Method in class com.bayesserver.data.DataTable: Copies both the structure and data in the DataTable.
copy() - Method in interface com.bayesserver.Distribution: Creates a copy of the distribution.
copy() - Method in interface com.bayesserver.Expression: Creates a copy of the expression.
copy() - Method in class com.bayesserver.FunctionVariableExpression: Creates a copy of the expression.
copy() - Method in class com.bayesserver.inference.QueryDistribution: Copies this instance, creating a copy of the distribution as well.
copy() - Method in class com.bayesserver.inference.QueryFunction: Copies this instance, creating a copy of the function output as well.
copy() - Method in class com.bayesserver.inference.QueryFunctionOutput: Creates a copy of this instance.
copy() - Method in class com.bayesserver.Network: Makes a copy of the network.
copy() - Method in class com.bayesserver.Node: Makes a copy of this instance.
copy() - Method in class com.bayesserver.NodeDistributionOptions: Copies this instance.
copy() - Method in class com.bayesserver.NodeGroup: Makes a copy of this instance.
copy() - Method in class com.bayesserver.State: Copies this instance.
copy() - Method in class com.bayesserver.Table: Creates a copy of the distribution.
copy() - Method in class com.bayesserver.TableExpression: Creates a copy of the expression.
copy() - Method in class com.bayesserver.Variable: Copies this instance.
copy(boolean) - Method in class com.bayesserver.data.DataTable: Copies the structure and optionally the data in the DataTable.
copy(Evidence) - Method in class com.bayesserver.inference.DefaultEvidence: Replaces the current evidence, with that from another Evidence instance.
copy(Evidence) - Method in interface com.bayesserver.inference.Evidence: Replaces the current evidence, with that from another Evidence instance.
copy(Evidence, Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Replaces the current evidence for an individual variable, with that from another Evidence instance.
copy(Evidence, Variable) - Method in interface com.bayesserver.inference.Evidence: Replaces the current evidence for an individual variable, with that from another Evidence instance.
copy(Evidence, Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Replaces the current evidence for an individual variable at a specific time, with that from another Evidence instance.
copy(Evidence, Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Replaces the current evidence for an individual variable at a specific time, with that from another Evidence instance.
copy(Node, Node, int) - Method in class com.bayesserver.Link: Creates a new link, copying the properties from this instance, such as Link.getDescription() and Link.getCustomProperties().
copy(Variable) - Method in class com.bayesserver.data.VariableReference: Creates a copy of this instance, but based on a different variable.
copy(Integer) - Method in class com.bayesserver.CLGaussian: Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
copy(Integer) - Method in interface com.bayesserver.Distribution: Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
copy(Integer) - Method in class com.bayesserver.Table: Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
copyFrom(double[]) - Method in class com.bayesserver.Table: Copies values from the array into the table.
copyFrom(double[]) - Method in class com.bayesserver.TableAccessor: Copies values from an array into the underlying Table using the variable ordering of the TableAccessor, not the Table.getSortedVariables().
copyFrom(double[]) - Method in class com.bayesserver.TableIterator: Resets the iterator and then copies values from an array into the underlying Table using the variable ordering of the TableIterator, not the Table.getSortedVariables().
copyFrom(CLGaussian) - Method in class com.bayesserver.CLGaussian: Copies the values from the [source] distribution to this instance.
copyTo(double[]) - Method in class com.bayesserver.Table: Copies the table values to an array.
copyTo(Table) - Method in class com.bayesserver.Table: Copies all values from this instance to the destination Table.
Correlation - Class in com.bayesserver.analysis: Methods to convert covariance matrices to correlation matrices.
create(DataReaderCommand, String, String) - Static method in class com.bayesserver.analysis.RegressionStatistics: Initializes a new instance of the RegressionStatistics class.
create(DataReaderCommand, String, String, String) - Static method in class com.bayesserver.analysis.ConfusionMatrix: Initializes a new instance of the ConfusionMatrix class.
create(DataReaderCommand, String, String, String) - Static method in class com.bayesserver.analysis.RegressionStatistics: Initializes a new instance of the RegressionStatistics class.
create(Network) - Method in class com.bayesserver.data.DataTableEvidenceReaderCommandFactory: Create an evidence reader command, based on a specific network which may be a copy of the original.
create(Network) - Method in interface com.bayesserver.data.EvidenceReaderCommandFactory: Create an evidence reader command, based on a specific network which may be a copy of the original.
create(String, String, String, Comparable, DataReaderCommand) - Static method in class com.bayesserver.analysis.LiftChart: Creates a lift chart, used to measure predictive performance.
createDataReader() - Method in class com.bayesserver.data.DataTable: Create a DataReader based on the DataTable.
createDataReader(T) - Method in interface com.bayesserver.data.distributed.DataPartition: Create a data reader for this distributed partition.
createEvidenceReader(T) - Method in interface com.bayesserver.data.distributed.EvidencePartition: Create an evidence reader for this distributed mapper.
createEvidenceReaderCommand(Network, DataPartitioning) - Method in interface com.bayesserver.analysis.ClusterCountActions: A user supplied function to create an evidence reader command based on a copy of the original network.
createInferenceEngine(Network) - Method in class com.bayesserver.causal.BackdoorInferenceFactory: Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.causal.FrontDoorInferenceFactory: Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in interface com.bayesserver.inference.InferenceFactory: Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.LikelihoodSamplingInferenceFactory: Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.LoopyBeliefInferenceFactory: Creates an instance of an inference algorithm, with the [network] as it's target.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.RelevanceTreeInferenceFactory: Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
createInferenceEngine(Network) - Method in class com.bayesserver.inference.VariableEliminationInferenceFactory: Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
createPartitioned(Network, DataPartitioning, int) - Method in class com.bayesserver.data.DataTableEvidenceReaderCommandFactory: Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
createPartitioned(Network, DataPartitioning, int) - Method in interface com.bayesserver.data.EvidenceReaderCommandFactory: Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
createQueryOptions() - Method in class com.bayesserver.causal.BackdoorInferenceFactory: Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.causal.FrontDoorInferenceFactory: Creates options that govern how each query is performed.
createQueryOptions() - Method in interface com.bayesserver.inference.InferenceFactory: Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.inference.LikelihoodSamplingInferenceFactory: Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.inference.LoopyBeliefInferenceFactory: Creates options that govern how each query is performed.
createQueryOptions() - Method in class com.bayesserver.inference.RelevanceTreeInferenceFactory: Creates a RelevanceTreeQueryOptions instance that governs the calculations performed by the RelevanceTreeInference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput) method.
createQueryOptions() - Method in class com.bayesserver.inference.VariableEliminationInferenceFactory: Creates a VariableEliminationQueryOptions instance that governs the calculations performed by the VariableEliminationInference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput) method.
createQueryOutput() - Method in class com.bayesserver.causal.BackdoorInferenceFactory: Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.causal.FrontDoorInferenceFactory: Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in interface com.bayesserver.inference.InferenceFactory: Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.LikelihoodSamplingInferenceFactory: Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.LoopyBeliefInferenceFactory: Creates an object that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.RelevanceTreeInferenceFactory: Creates a RelevanceTreeQueryOutput instance that collects information about each query, in addition to the distributions.
createQueryOutput() - Method in class com.bayesserver.inference.VariableEliminationInferenceFactory: Creates a VariableEliminationQueryOutput instance that collects information about each query, in addition to the distributions.
CROSS_LOG_LIKELIHOOD - com.bayesserver.learning.structure.ScoreMethod: The score is the log-likelihood calculated on unseen data using cross validation.
CrossValidation - Class in com.bayesserver.data: Allows test metrics/scores to be calculated using cross validation.
CrossValidationActions - Interface in com.bayesserver.data: Actions which the caller must implement to use Cross Validation.
CrossValidationCombineMethod - Enum in com.bayesserver.data: Ways of combining cross validation test results to form an overall cross validation score.
CrossValidationNetwork - Interface in com.bayesserver.data: The result of learning on a single cross validation training partitioning.
CrossValidationOutput - Class in com.bayesserver.data: Details of a Cross-Validation partition.
CrossValidationScore - Interface in com.bayesserver.data: Interface for cross validation scores.
CrossValidationTestResult - Interface in com.bayesserver.data: Interface for cross validation test results.
CustomProperty - Class in com.bayesserver: Stores a custom property.
CustomProperty(String) - Constructor for class com.bayesserver.CustomProperty: Initializes a new instance of the CustomProperty class.
CustomProperty(String, String) - Constructor for class com.bayesserver.CustomProperty: Initializes a new instance of the CustomProperty class.
CustomPropertyCollection - Class in com.bayesserver: Stores custom properties for a variety of objects.

D

D_CONNECTED - com.bayesserver.analysis.DSeparationCategory: The test node is D-Connected to the source nodes.
D_SEPARATED - com.bayesserver.analysis.DSeparationCategory: The test node is D-Separated from the source nodes.
Dag - Class in com.bayesserver: Includes methods for testing whether a network is a Directed Acyclic Graph (DAG).
DatabaseDataReaderCommand - Class in com.bayesserver.data: Provides a default implementation of DataReaderCommand for reading databases.
DatabaseDataReaderCommand(String, String) - Constructor for class com.bayesserver.data.DatabaseDataReaderCommand: Initializes a new instance of the DatabaseDataReaderCommand class.
DatabaseDataReaderCommand(String, String, int) - Constructor for class com.bayesserver.data.DatabaseDataReaderCommand: Initializes a new instance of the DatabaseDataReaderCommand class.
DatabaseDataReaderCommand(String, String, String, String) - Constructor for class com.bayesserver.data.DatabaseDataReaderCommand: Initializes a new instance of the DatabaseDataReaderCommand class.
DataColumn - Class in com.bayesserver.data: Class that represents an memory column of data.
DataColumn(String, Class) - Constructor for class com.bayesserver.data.DataColumn: Creates a new instance of the DataColumn class.
DataColumnCollection - Class in com.bayesserver.data: Represents a collection of columns in a DataTable, a simple in-memory data store.
DataIOException - Exception in com.bayesserver.data: Raised when an error occurs reading data from or writing data to a database, a file or other source.
DataIOException() - Constructor for exception com.bayesserver.data.DataIOException: Initializes a new instance of the DataIOException class.
DataIOException(String) - Constructor for exception com.bayesserver.data.DataIOException: Initializes a new instance of the DataIOException class with a specified error message.
DataIOException(String, Throwable) - Constructor for exception com.bayesserver.data.DataIOException: Initializes a new instance of the DataIOException class with a specified error message and a reference to the inner exception that is the cause of this exception.
DataIOException(Throwable) - Constructor for exception com.bayesserver.data.DataIOException: Initializes a new instance of the DataIOException class with a reference to the inner exception that is the cause of this exception.
DataPartition<T> - Interface in com.bayesserver.data.distributed: Interface used by distributed processes that read data.
DataPartitioning - Class in com.bayesserver.data: Determines how data is partitioned.
DataPartitioning(int, DataPartitionMethod, int) - Constructor for class com.bayesserver.data.DataPartitioning: Initializes a new instance of the DataPartitioning class.
DataPartitionMethod - Enum in com.bayesserver.data: Determines whether data is included or excluded from a DataPartitioning.
DataProgress - Interface in com.bayesserver.data: Reports progress on the number of cases read.
DataProgressEventArgs - Class in com.bayesserver.data: Used to provide progress on how many cases have been read.
DataProgressEventArgs() - Constructor for class com.bayesserver.data.DataProgressEventArgs
DataReader - Interface in com.bayesserver.data: Interface for reading data row by row.
DataReaderCommand - Interface in com.bayesserver.data: Interface used by EvidenceReader in order to read data multiple times.
DataReaderCommandFiltered - Class in com.bayesserver.data: Wraps an existing data reader command while filtering records.
DataReaderCommandFiltered(DataReaderCommand, DataReaderFilter) - Constructor for class com.bayesserver.data.DataReaderCommandFiltered: Initializes a new instance of the DataReaderCommandFiltered class.
DataReaderFilter - Interface in com.bayesserver.data: Interface to determine whether records should be filtered in a data reader.
DataReaderFiltered - Class in com.bayesserver.data: Wraps an existing data reader while filtering records.
DataReaderFiltered(DataReader, DataReaderFilter) - Constructor for class com.bayesserver.data.DataReaderFiltered: Initializes a new instance of the DataReaderFiltered class.
DataRecord - Interface in com.bayesserver.data: Interface for reading the values from a row of data.
DataRow - Class in com.bayesserver.data: Represents a row of data in a DataTable, a simple in-memory data store.
DataRow(Object[]) - Constructor for class com.bayesserver.data.DataRow: Creates a new instance of a DataRow with the given items.
DataRowCollection - Class in com.bayesserver.data: A collection of rows in a DataTable, a simple in-memory data store.
DataSampler - Class in com.bayesserver.data.sampling: Generates samples from a Bayesian network or Dynamic Bayesian network.
DataSampler(Network) - Constructor for class com.bayesserver.data.sampling.DataSampler: Initializes a new instance of the DataSampler class.
DataSampler(Network, Evidence) - Constructor for class com.bayesserver.data.sampling.DataSampler: Initializes a new instance of the DataSampler class.
DataSamplingOptions - Class in com.bayesserver.data.sampling: Options for data sampling.
DataSamplingOptions() - Constructor for class com.bayesserver.data.sampling.DataSamplingOptions: Initializes a new instance of DataSamplingOptions.
DataTable - Class in com.bayesserver.data: A simple in memory data structure which can be used as an alternative to a data store (such as a database).
DataTable() - Constructor for class com.bayesserver.data.DataTable: Creates a new instance of the DataTable class.
DataTableDataReaderCommand - Class in com.bayesserver.data: A DataReaderCommand backed by a DataTable.
DataTableDataReaderCommand(DataTable) - Constructor for class com.bayesserver.data.DataTableDataReaderCommand: Creates a new instance, based on a DataTable.
DataTableEvidenceReaderCommandFactory - Class in com.bayesserver.data: A default implementation of EvidenceReaderCommandFactory based on a DataTable and a simple partitioning scheme based on a partition column.
DataTableEvidenceReaderCommandFactory(DataTable, List<VariableReference>, ReaderOptions, String) - Constructor for class com.bayesserver.data.DataTableEvidenceReaderCommandFactory: Initializes a new instance of the DataTableEvidenceReaderCommandFactory class.
DataTableEvidenceReaderCommandFactory(DataTable, List<VariableReference>, ReaderOptions, String, DataTable, List<VariableReference>, TemporalReaderOptions, String) - Constructor for class com.bayesserver.data.DataTableEvidenceReaderCommandFactory: Initializes a new instance of the DataTableEvidenceReaderCommandFactory class.
DataTableEvidenceReaderCommandFactory(DataTable, List<VariableReference>, TemporalReaderOptions, String) - Constructor for class com.bayesserver.data.DataTableEvidenceReaderCommandFactory: Initializes a new instance of the DataTableEvidenceReaderCommandFactory class.
DataTableReader - Class in com.bayesserver.data: Allows a DataTable to be read as a DataReader.
DataTableReader(DataTable) - Constructor for class com.bayesserver.data.DataTableReader: Creats a new DataTableReader instance, backed by a DataTable, a simple in-memory data store.
DECISION - com.bayesserver.VariableKind: A decision variable, which can be used for decision making based on utilities.
DecisionAlgorithm - Enum in com.bayesserver.inference: The type of algorithm to use when a network has decision nodes.
DecisionPostProcessingMethod - Enum in com.bayesserver.learning.parameters: The type of post processing to be applied to the distributions of decision nodes at the end of parameter learning.
decompose(Network, DecomposeOptions) - Static method in class com.bayesserver.Decomposer: Decomposes a Bayesian network containing nodes with multiple variables into its single variable node equivalent.
DecomposeOptions - Class in com.bayesserver: Options used by the Decomposer class.
DecomposeOptions() - Constructor for class com.bayesserver.DecomposeOptions
DecomposeOutput - Class in com.bayesserver: Contains information returned by Decomposer.decompose(com.bayesserver.Network, com.bayesserver.DecomposeOptions).
Decomposer - Class in com.bayesserver: Contains methods to decompose nodes with multiple variables into their single variable equivalents.
DEFAULT - com.bayesserver.inference.DecisionAlgorithm: Use the default algorithm.
DefaultCancellation - Class in com.bayesserver: Class for canceling long running operations.
DefaultCancellation() - Constructor for class com.bayesserver.DefaultCancellation
DefaultCrossValidationNetwork - Class in com.bayesserver.data: Default basic implementation of ICrossValidationNetwork.
DefaultCrossValidationNetwork(Network) - Constructor for class com.bayesserver.data.DefaultCrossValidationNetwork: Initializes a new instance of the DefaultCrossValidationNetwork class, with a network.
DefaultCrossValidationScore - Class in com.bayesserver.data: A default simple implementation of ICrossValidationScore.
DefaultCrossValidationScore(double) - Constructor for class com.bayesserver.data.DefaultCrossValidationScore: Initializes a new instance of the DefaultCrossValidationScore class.
DefaultCrossValidationTestResult - Class in com.bayesserver.data: A simple default implementation of CrossValidationTestResult.
DefaultCrossValidationTestResult(double, Object, Double) - Constructor for class com.bayesserver.data.DefaultCrossValidationTestResult: Initializes a new instance of the DefaultCrossValidationTestResult class.
DefaultDataReader - Class in com.bayesserver.data: Reads and validates non temporal and/or temporal data.
DefaultDataReader(DataReader, ReaderOptions) - Constructor for class com.bayesserver.data.DefaultDataReader: Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, ReaderOptions, DataReader, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultDataReader: Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, ReaderOptions, DataReader, TemporalReaderOptions, List<NestedDataReader>) - Constructor for class com.bayesserver.data.DefaultDataReader: Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, ReaderOptions, List<NestedDataReader>) - Constructor for class com.bayesserver.data.DefaultDataReader: Initializes a new instance of the DefaultDataReader class.
DefaultDataReader(DataReader, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultDataReader: Initializes a new instance of the DefaultDataReader class.
DefaultEvidence - Class in com.bayesserver.inference: Represents the evidence, or case data (e.g.
DefaultEvidence(DefaultEvidence) - Constructor for class com.bayesserver.inference.DefaultEvidence: Initializes a new instance of the DefaultEvidence class, copying data from an existing DefaultEvidence object.
DefaultEvidence(Evidence) - Constructor for class com.bayesserver.inference.DefaultEvidence: Initializes a new instance of the DefaultEvidence class, and copies the evidence from another instance.
DefaultEvidence(Network) - Constructor for class com.bayesserver.inference.DefaultEvidence: Initializes a new instance of the DefaultEvidence class, with the target Bayesian network.
DefaultEvidenceReader - Class in com.bayesserver.data: Provides a default implementation of EvidenceReader, used in Bayes Server for tasks such as parameter learning.
DefaultEvidenceReader(DataReader, List<VariableReference>, ReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReader: Initializes a new instance of the DefaultEvidenceReader class.
DefaultEvidenceReader(DataReader, List<VariableReference>, ReaderOptions, DataReader, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReader: Initializes a new instance of the DefaultEvidenceReader class, supporting both temporal and non temporal data.
DefaultEvidenceReader(DataReader, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReader: Initializes a new instance of the DefaultEvidenceReader class.
DefaultEvidenceReaderCommand - Class in com.bayesserver.data: Creates instances of EvidenceReader on demand.
DefaultEvidenceReaderCommand(DataReaderCommand, List<VariableReference>, ReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReaderCommand: Initializes a new instance of the DefaultEvidenceReaderCommand class.
DefaultEvidenceReaderCommand(DataReaderCommand, List<VariableReference>, ReaderOptions, DataReaderCommand, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReaderCommand: Initializes a new instance of the DefaultEvidenceReaderCommand class, supporting both temporal and non temporal data.
DefaultEvidenceReaderCommand(DataReaderCommand, List<VariableReference>, TemporalReaderOptions) - Constructor for class com.bayesserver.data.DefaultEvidenceReaderCommand: Initializes a new instance of the DefaultEvidenceReaderCommand class.
DefaultQueryDistributionCollection - Class in com.bayesserver.inference: The collection of distributions to be calculated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
DefaultQueryDistributionCollection(Network) - Constructor for class com.bayesserver.inference.DefaultQueryDistributionCollection: Initializes a new instance of the DefaultQueryDistributionCollection class, passing the target Bayesian network as a parameter.
DefaultQueryFunctionCollection - Class in com.bayesserver.inference: The collection of functions to be evaluated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
DefaultQueryFunctionCollection(Network) - Constructor for class com.bayesserver.inference.DefaultQueryFunctionCollection: Initializes a new instance of the DefaultQueryFunctionCollection class, passing the target Bayesian network as a parameter.
DefaultReadOptions - Class in com.bayesserver.data: Provides a default implementation of ReadOptions.
DefaultReadOptions() - Constructor for class com.bayesserver.data.DefaultReadOptions: Initializes a new instance of the DefaultReadOptions class.
DefaultReadOptions(boolean) - Constructor for class com.bayesserver.data.DefaultReadOptions: Initializes a new instance of the DefaultReadOptions class.
DESCENDING - com.bayesserver.data.discovery.SortOrder: Discrete states should be sorted in descending order.
DESCENDING - com.bayesserver.NoisyOrder: The states of the parent affect the noisy node states in decreasing order.
DESCENDING_FREQUENCY - com.bayesserver.data.discovery.SortOrder: Discrete states should be sorted in order of descending frequency of ocuurence in the data.
DesignEvidenceKind - Enum in com.bayesserver.optimization: The type of evidence the optimizer should use.
DesignState - Class in com.bayesserver.optimization: An input to the optimization algorithm.
DesignState(State, Double, Double) - Constructor for class com.bayesserver.optimization.DesignState: Initializes a new instance of the com.bayesserver.optization.DesignState class.
DesignVariable - Class in com.bayesserver.optimization: Specifies on or more inputs to the optimization algorithm.
DesignVariable(Variable, Double, Double, boolean) - Constructor for class com.bayesserver.optimization.DesignVariable: Initializes a new instance of the com.bayesserver.optization.DesignVariable class, automatically generating the necessary design states.
DesignVariable(Variable, Double, Double, boolean, InterventionType) - Constructor for class com.bayesserver.optimization.DesignVariable: Initializes a new instance of the DesignVariable class, automatically generating the necessary design states.
DesignVariable(Variable, List<DesignState>, boolean) - Constructor for class com.bayesserver.optimization.DesignVariable: Initializes a new instance of the DesignVariable class.
DesignVariable(Variable, List<DesignState>, DesignEvidenceKind, boolean, InterventionType) - Constructor for class com.bayesserver.optimization.DesignVariable: Initializes a new instance of the DesignVariable class.
detect(Network, Variable, List<Integer>, ClusterCountActions, ClusterCountOptions) - Static method in class com.bayesserver.analysis.ClusterCount: Determine the number of clusters (discrete states of a latent variable) using cross validation.
detect(List<Variable>, EvidenceReaderCommand, Variable, FeatureSelectionOptions) - Static method in class com.bayesserver.learning.structure.FeatureSelection: Determines which variables are likely to be good features (predictors) of a target variable.
DIFFERENCE - com.bayesserver.inference.QueryComparison: The difference between the current queried value and the value calculated using Base evidence.
DIRECT - com.bayesserver.inference.CausalEffectKind: The direct causal effect, which only includes the effect of direct links between treatments (X) and outcomes (Y).
DISCRETE - com.bayesserver.VariableValueType: Discrete/Categorical/Nominal data.
DiscretePriorMethod - Enum in com.bayesserver.learning.parameters: The type of discrete prior to use for discrete distributions during parameter learning.
DiscretizationAlgoOptions - Class in com.bayesserver.data.discovery: Options for a discretization algorithm.
DiscretizationAlgoOptions() - Constructor for class com.bayesserver.data.discovery.DiscretizationAlgoOptions: Initializes a new instance of the com.bayesserver.data.discovery.DiscretizationAlgorithmOptions class.
DiscretizationAlgoOptions(String) - Constructor for class com.bayesserver.data.discovery.DiscretizationAlgoOptions: Initializes a new instance of the com.bayesserver.data.discovery.DiscretizationAlgorithmOptions class.
DiscretizationColumn - Class in com.bayesserver.data.discovery: Identifies a column of data and how it is to be discretized.
DiscretizationColumn(String) - Constructor for class com.bayesserver.data.discovery.DiscretizationColumn: Initializes a new instance of the DiscretizationColumn class.
DiscretizationInfo - Class in com.bayesserver.data.discovery: Discretization information for column of data, returned from a discretization algorithm.
DiscretizationMethod - Enum in com.bayesserver.data.discovery: The method (algorithm) to use for discretization of continuous data.
DiscretizationOptions - Class in com.bayesserver.data.discovery: Options that determine whether and how continuous data should be discretized.
DiscretizationOptions() - Constructor for class com.bayesserver.data.discovery.DiscretizationOptions
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in class com.bayesserver.data.discovery.Clustering: Discretizes one or more data columns, that may contain missing (null) values.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in interface com.bayesserver.data.discovery.Discretize: Discretizes one or more data columns, that may contain missing (null) values.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in class com.bayesserver.data.discovery.EqualFrequencies: Discretizes one or more data columns, that may contain missing (null) values.
discretize(DataReaderCommand, List<DiscretizationColumn>, DiscretizationAlgoOptions) - Method in class com.bayesserver.data.discovery.EqualIntervals: Discretizes one or more data columns, that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.Clustering: Discretizes unsorted continuous data that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in interface com.bayesserver.data.discovery.Discretize: Discretizes unsorted continuous data that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualFrequencies: Discretizes unsorted continuous data that may contain missing (null) values.
discretize(Iterable<Double>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualIntervals: Discretizes unsorted continuous data that may contain missing (null) values.
Discretize - Interface in com.bayesserver.data.discovery: Interface which a discretization algorithm must implement.
DiscretizeProgress - Interface in com.bayesserver.data.discovery: Interface to provide progress information during discretization.
DiscretizeProgressInfo - Interface in com.bayesserver.data.discovery: Interface to provide progress information during discretization.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.Clustering: Discretizes unsorted weighted continuous data that may contain missing (null) values.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in interface com.bayesserver.data.discovery.Discretize: Discretizes unsorted weighted continuous data that may contain missing (null) values.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualFrequencies: Discretizes unsorted weighted continuous data that may contain missing (null) values.
discretizeWeighted(Iterable<WeightedValue>, DiscretizationOptions, String) - Method in class com.bayesserver.data.discovery.EqualIntervals: Discretizes unsorted weighted continuous data that may contain missing (null) values.
DisjunctiveCauseCriterion - Class in com.bayesserver.causal: Validates inputs for the Disjunctive cause adjustment.
DisjunctiveCauseCriterion(Network) - Constructor for class com.bayesserver.causal.DisjunctiveCauseCriterion: Initializes a new instance of the DisjunctiveCauseCriterion class.
DisjunctiveCauseCriterionOptions - Class in com.bayesserver.causal: Options for Disjunctive-cause Criterion validation.
DisjunctiveCauseCriterionOptions() - Constructor for class com.bayesserver.causal.DisjunctiveCauseCriterionOptions
DisjunctiveCauseCriterionOutput - Class in com.bayesserver.causal: The output from the Disjunctive-cause criterion, which is simply an adjustment set which includes all causes of treatments (X) or causes of outcomes (Y) or causes of both.
DisjunctiveCauseInference - Class in com.bayesserver.causal: Estimates the causal effect, using the 'Disjunctive Cause Criterion' adjustment formula to avoid confounding bias.
DisjunctiveCauseInference(Network) - Constructor for class com.bayesserver.causal.DisjunctiveCauseInference: Initializes a new instance of the DisjunctiveCauseInference class.
DisjunctiveCauseInferenceFactory - Class in com.bayesserver.causal: Uses the factory design pattern to create inference related objects for the Disjunctive cause algorithm.
DisjunctiveCauseInferenceFactory() - Constructor for class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Initializes a new instance of the DisjunctiveCauseInferenceFactory class.
DisjunctiveCauseInferenceFactory(QueryLifecycle) - Constructor for class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Initializes a new instance of the DisjunctiveCauseInferenceFactory class, with an optional lifecycle instance.
DisjunctiveCauseQueryOptions - Class in com.bayesserver.causal: Options for DisjunctiveCauseInference.
DisjunctiveCauseQueryOptions() - Constructor for class com.bayesserver.causal.DisjunctiveCauseQueryOptions: Initializes a new instance of the DisjunctiveCauseQueryOptions class.
DisjunctiveCauseQueryOptions(DisjunctiveCauseSet, AdjustmentSet) - Constructor for class com.bayesserver.causal.DisjunctiveCauseQueryOptions: Initializes a new instance of the DisjunctiveCauseQueryOptions class.
DisjunctiveCauseQueryOutput - Class in com.bayesserver.causal: Returns any information, in addition to the distributions, that is requested from a query.
DisjunctiveCauseQueryOutput() - Constructor for class com.bayesserver.causal.DisjunctiveCauseQueryOutput: Initializes a new instance of the DisjunctiveCauseQueryOutput class.
DisjunctiveCauseSet - Class in com.bayesserver.causal: Identifies sets of nodes used by the Disjunctive Cause Criterion algorithm.
DisjunctiveCauseSet(DisjunctiveCauseSetNode...) - Constructor for class com.bayesserver.causal.DisjunctiveCauseSet: Initializes a new instance of the DisjunctiveCauseSet class.
DisjunctiveCauseSet(List<DisjunctiveCauseSetNode>) - Constructor for class com.bayesserver.causal.DisjunctiveCauseSet: Initializes a new instance of the DisjunctiveCauseSet class.
DisjunctiveCauseSetNode - Class in com.bayesserver.causal: Represents a node in a set used by the Disjunctive Cause Criterion algorithm.
DisjunctiveCauseSetNode(Node) - Constructor for class com.bayesserver.causal.DisjunctiveCauseSetNode: Initializes a new instance of the DisjunctiveCauseSetNode class.
DisjunctiveCauseSetNode(Node, Integer) - Constructor for class com.bayesserver.causal.DisjunctiveCauseSetNode: Initializes a new instance of the DisjunctiveCauseSetNode class.
DisjunctiveCauseValidationOptions - Class in com.bayesserver.causal: Options for Disjunctive-cause criterion validation.
DisjunctiveCauseValidationOptions(AdjustmentSet) - Constructor for class com.bayesserver.causal.DisjunctiveCauseValidationOptions: Initializes a new instance of the DisjunctiveCauseValidationOptions class.
distribute(T) - Method in interface com.bayesserver.Distributer: The implementor should distribute the processing.
DistributedMapperContext - Class in com.bayesserver.learning.parameters: Contains information used during distributed parameter learning.
DistributedMapperContext() - Constructor for class com.bayesserver.learning.parameters.DistributedMapperContext
Distributer<T> - Interface in com.bayesserver
DistributerContext - Class in com.bayesserver.learning.parameters: Contains contextual information about the process/iteration being distributed.
Distribution - Interface in com.bayesserver: Interface specifying the required methods and properties for a probability distribution.
distributionChanged(Node, NodeDistributionKey, NodeDistributionKind, Distribution, Distribution) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
DistributionExpression - Interface in com.bayesserver: Base interface for expressions that generate distributions.
DistributionMonitoring - Enum in com.bayesserver.learning.parameters: Indicates which distribution to monitor during learning.
DistributionSpecification - Class in com.bayesserver.learning.parameters: Identifies a node's distribution to learn, and options for learning.
DistributionSpecification(Node) - Constructor for class com.bayesserver.learning.parameters.DistributionSpecification: Initializes a new instance of the DistributionSpecification class.
DistributionSpecification(Node, int) - Constructor for class com.bayesserver.learning.parameters.DistributionSpecification: Initializes a new instance of the DistributionSpecification class.
DistributionSpecification(Node, NodeDistributionKey) - Constructor for class com.bayesserver.learning.parameters.DistributionSpecification: Initializes a new instance of the DistributionSpecification class.
divergence(Distribution, Distribution, LogarithmBase) - Static method in class com.bayesserver.statistics.JensenShannon: Calculates the Jensen Shannon divergence between two distributions.
divergence(Distribution, Distribution, LogarithmBase) - Static method in class com.bayesserver.statistics.KullbackLeibler: Calculates the Kullback-Leibler divergence D(P||Q).
divide(CLGaussian) - Method in class com.bayesserver.CLGaussian: Creates a new distribution by dividing this instance by the [subset].
divide(Distribution) - Method in class com.bayesserver.CLGaussian: Creates a new distribution by dividing this instance by the [subset].
divide(Distribution) - Method in interface com.bayesserver.Distribution: Creates a new distribution by dividing the instance by the specified subset.
divide(Distribution) - Method in class com.bayesserver.Table: Creates a new distribution by dividing this instance by the [subset].
divideByPrior(Table, Table) - Static method in class com.bayesserver.inference.SoftEvidence: Divides target soft evidence by an existing prior distribution or query.
divideInPlace(Table) - Method in class com.bayesserver.Table: Divides this instance in place by the [subset].
DO - com.bayesserver.inference.InterventionType: An intervention (do-operator)
DOUBLE - com.bayesserver.ExpressionReturnType: Expression returns a double precision floating point number.
DOUBLE_INTERVAL - com.bayesserver.StateValueType: The State value is an interval specified using double precision numbers.
DSeparation - Class in com.bayesserver.analysis: Contains methods to calculate D-Separation.
DSeparationCategory - Enum in com.bayesserver.analysis: The result of a D-Separation test.
DSeparationOptions - Class in com.bayesserver.analysis: Options for calculating D-Separation.
DSeparationOptions() - Constructor for class com.bayesserver.analysis.DSeparationOptions
DSeparationOutput - Class in com.bayesserver.analysis: Contains the results of a test for D-Separation.
DSeparationTestResult - Class in com.bayesserver.analysis: The result of a D-Separation check for a test node.
DSeparationTestResultCollection - Class in com.bayesserver.analysis: Collection of D-Separation test results.

E

EffectsAnalysis - Class in com.bayesserver.causal: Calculates the causal effect on a target, varying for different treatment values.
EffectsAnalysisOptions - Class in com.bayesserver.causal: Options for an effects analysis.
EffectsAnalysisOptions() - Constructor for class com.bayesserver.causal.EffectsAnalysisOptions
EffectsAnalysisOutput - Class in com.bayesserver.causal: The results of an effects analysis.
EffectsAnalysisOutputItem - Class in com.bayesserver.causal: The result of an effects analysis for a particular treatment value.
EliminationDefinition - Class in com.bayesserver.inference: Identifies a node that is eliminated during exact inference.
EliminationDefinitionCollection - Class in com.bayesserver.inference: A list of elminated nodes during inference.
EmpiricalDensity - Interface in com.bayesserver.analysis: Represents an empirical density function, which can represent arbitrary univariate distributions.
EmptyStringAction - Enum in com.bayesserver.data: Determines the action to take when an empty string is encountered.
end(QueryLifecycleEnd) - Method in interface com.bayesserver.inference.QueryLifecycle: Called after the query is computed.
endUpdate() - Method in class com.bayesserver.inference.DefaultEvidence: Enables change notifications (if available).
endUpdate() - Method in interface com.bayesserver.inference.Evidence: Enables change notifications (if available).
Entropy - Class in com.bayesserver.statistics: Calculates entropy, joint entropy or conditional entropy, which can be used to determine the uncertainty in the states of a discrete distribution.
entrySet() - Method in class com.bayesserver.NodeDistributionExpressions
entrySet() - Method in class com.bayesserver.NodeDistributions
enumerate(int[], CombinationAction, CombinationOptions) - Static method in class com.bayesserver.analysis.Combinations: Enumerates the combinations for a set of counts.
enumerate(List<Variable>, CombinationAction, CombinationOptions) - Static method in class com.bayesserver.analysis.Combinations: Enumerates the state combinations for a set of variables.
EQUAL_FREQUENCIES - com.bayesserver.data.discovery.DiscretizationMethod: Discretize such that each bin contains a similar number of items.
EQUAL_INTERVALS - com.bayesserver.data.discovery.DiscretizationMethod: Discretize using equal width intervals.
EqualFrequencies - Class in com.bayesserver.data.discovery: Discretizes continuous data in bins, such that each bin contain a similar number of data points.
EqualFrequencies() - Constructor for class com.bayesserver.data.discovery.EqualFrequencies
EqualIntervals - Class in com.bayesserver.data.discovery: Discretizes continuous data in bins, such that the bins have equal size.
EqualIntervals() - Constructor for class com.bayesserver.data.discovery.EqualIntervals
equals(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributionKey: Indicates whether the current object is equal to another object of the same type.
equals(Object) - Method in class com.bayesserver.Bounds
equals(Object) - Method in class com.bayesserver.causal.CausalNode
equals(Object) - Method in class com.bayesserver.data.discovery.WeightedValue
equals(Object) - Method in class com.bayesserver.inference.EvidenceTypes
equals(Object) - Method in class com.bayesserver.Interval
equals(Object) - Method in class com.bayesserver.NodeDistributionKey
equals(Object) - Method in class com.bayesserver.StateContext
equals(Object) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
equals(Object) - Method in class com.bayesserver.ValidationOptions
evaluate(double) - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Evaluates the Sensitivity function P(h|e)(t) = (alpha * t + beta) / (gamma * t + delta)
evaluate(double, double) - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Evaluates the Sensitivity function P(h|e)(t1,t2) = (alpha1 * t1 * t2 + beta1 * t1 + gamma1 * t2 + delta1) / (alpha2 * t1 * t2 + beta2 * t1 + gamma2 * t2 + delta2)
evaluateDeriv(double) - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Evaluates the partial derivative of the Sensitivity function with respect to t = ((alpha*delta)-(beta*gamma))/((gamma*t + delta)^2)
Evidence - Interface in com.bayesserver.inference: Represents the evidence, or case data (e.g.
EvidencePartition<T> - Interface in com.bayesserver.data.distributed: Interface used by distributed processes that read evidence.
EvidenceReader - Interface in com.bayesserver.data: A data set iterator, that can be read multiple times.
EvidenceReaderCommand - Interface in com.bayesserver.data: Interface used to create instances of EvidenceReader.
EvidenceReaderCommandFactory - Interface in com.bayesserver.data: Creates evidence reader commands, for repeated iterating of a data set/partition of a data set.
EvidenceReaderEventArgs - Class in com.bayesserver.data: Contains a reference to a reader created by a reader command.
EvidenceType - Enum in com.bayesserver.inference: The type of evidence for a variable.
EvidenceTypes - Class in com.bayesserver.inference: Provides information about the type of evidence on a variable as well as whether it is an intervention (do operator) or not.
EvidenceTypes() - Constructor for class com.bayesserver.inference.EvidenceTypes
EXCLUDE - com.bayesserver.analysis.ImpactSubsetMethod: The maximum subset size is the maximum size of the subset of evidence being analyzed that is excluded.
EXCLUDE - com.bayesserver.analysis.LogLikelihoodAnalysisSubsetMethod: The maximum subset size is the maximum size of the subset of evidence being analyzed that is excluded.
EXCLUDE_PARTITION_DATA - com.bayesserver.data.DataPartitionMethod: The data set should exclude data from the partition.
ExcludedVariables - Class in com.bayesserver.data.sampling: Set of variables which should be excluded from an operation, such as missing data generation.
execute() - Method in interface com.bayesserver.MultipleIterator.Combination
execute(ImpactOutputItem) - Method in interface com.bayesserver.analysis.ImpactAction: Receives an individual impact item from the Impact.calculate(com.bayesserver.Network, com.bayesserver.Variable, com.bayesserver.State, com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.ImpactOptions) method.
execute(LogLikelihoodAnalysisOutputItem) - Method in interface com.bayesserver.analysis.LogLikelihoodAnalysisAction: Receives an individual Log-Likelihood analysis item from the LogLikelihoodAnalysis.calculate(com.bayesserver.Network, com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.LogLikelihoodAnalysisOptions) method.
execute(DataProgressEventArgs) - Method in interface com.bayesserver.data.DataProgress: Called by an algorithm to report progress on the number of cases read.
execute(EvidenceReaderEventArgs) - Method in interface com.bayesserver.data.ExecuteEvidenceReader: Called when an evidence reader is created.
execute(Integer[]) - Method in interface com.bayesserver.analysis.CombinationAction: Receives an individual combination from the Combinations.enumerate(java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.CombinationAction, com.bayesserver.analysis.CombinationOptions) method.
ExecuteEvidenceReader - Interface in com.bayesserver.data: Used to receive notification of a new Evidence reader being created from an evidence reader command.
executeReader() - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Returns an instance of IDataReader.
executeReader() - Method in interface com.bayesserver.data.DataReaderCommand: Returns an instance of IDataReader.
executeReader() - Method in class com.bayesserver.data.DataReaderCommandFiltered: Returns an instance of IDataReader.
executeReader() - Method in class com.bayesserver.data.DataTableDataReaderCommand: Creates a new DataReader backed by the DataTable.
executeReader() - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand: Returns an instance of IEvidenceReader which allows evidence to be iterated over.
executeReader() - Method in interface com.bayesserver.data.EvidenceReaderCommand: Returns an instance of IEvidenceReader which allows evidence to be iterated over.
executeReader() - Method in class com.bayesserver.data.timeseries.WindowDataReaderCommand: Returns an instance of IDataReader.
EXPERIENCE - com.bayesserver.NodeDistributionKind: A distribution which contains experience for the node, used in online learning.
Expression - Interface in com.bayesserver: Base interface for expressions.
ExpressionDistribution - Enum in com.bayesserver: Determines what happens when an expression is set on a node distribution.
ExpressionException - Exception in com.bayesserver: Exception raised during lexing, parsing or evaluation of an expression.
ExpressionException() - Constructor for exception com.bayesserver.ExpressionException: Initializes a new instance of the ExpressionException class.
ExpressionException(String) - Constructor for exception com.bayesserver.ExpressionException: Initializes a new instance of the ExpressionException class with a specified error message.
ExpressionException(String, Throwable) - Constructor for exception com.bayesserver.ExpressionException: Initializes a new instance of the ExpressionException class with a specified error message and a reference to the inner exception that is the cause of this exception.
ExpressionException(Throwable) - Constructor for exception com.bayesserver.ExpressionException: Initializes a new instance of the ExpressionException class with a reference to the inner exception that is the cause of this exception.
ExpressionReturnType - Enum in com.bayesserver: The type of value returned from an expression.

F

FADING - com.bayesserver.NodeDistributionKind: A table which contains fading values used in online learning.
FeatureSelection - Class in com.bayesserver.learning.structure: Contains methods to determine which variables are likely to be good features (predictors) or not.
FeatureSelectionOptions - Class in com.bayesserver.learning.structure: Options governing the tests carried out to determine whether variables are likely to be features (predictors) of a target variable.
FeatureSelectionOptions() - Constructor for class com.bayesserver.learning.structure.FeatureSelectionOptions
FeatureSelectionOutput - Class in com.bayesserver.learning.structure: Contains information returned by FeatureSelection.detect(java.util.List<com.bayesserver.Variable>, com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.Variable, com.bayesserver.learning.structure.FeatureSelectionOptions) about feature selection tests.
FeatureSelectionTest - Class in com.bayesserver.learning.structure: Contains information about a test carried out between a variable and a target to determine whether the variable is likely to be a feature or not.
finalize() - Method in class com.bayesserver.data.DefaultEvidenceReader
find(Node, Node) - Method in class com.bayesserver.NetworkLinkCollection: Finds a link from one node to another if it exists, otherwise returns null.
find(Node, Node, int) - Method in class com.bayesserver.NetworkLinkCollection: Finds a link from one node to another if it exists, otherwise returns null.
findByValue(Object) - Method in class com.bayesserver.StateCollection: Finds the state whose value/> matches the given [value], or null if a match is not found.
findForTime(int) - Method in class com.bayesserver.NodeDistributionExpressions: Finds the temporal distribution expression that is suitable for the time specified.
findForTime(int) - Method in class com.bayesserver.NodeDistributions: Finds the temporal distribution that is suitable for the time specified.
findForTime(int, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions: Finds the temporal distribution that is suitable for the time specified.
findForTime(int, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions: Finds the temporal distribution that is suitable for the time specified.
findForTimeWithOrder(int) - Method in class com.bayesserver.NodeDistributionExpressions: Finds the temporal distribution expression that is suitable for the time specified.
findForTimeWithOrder(int) - Method in class com.bayesserver.NodeDistributions: Finds the temporal distribution that is suitable for the time specified.
findForTimeWithOrder(int, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions: Finds the temporal distribution expression that is suitable for the time specified.
findForTimeWithOrder(int, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions: Finds the temporal distribution that is suitable for the time specified.
findStateByValue(Object) - Method in class com.bayesserver.Variable: Finds a state based on a state value.
FREQUENCIES - com.bayesserver.learning.parameters.DiscretePriorMethod: The prior for each combination is adjusted based on the frequency of that combination in the data.
fromCovariance(CLGaussian, int) - Static method in class com.bayesserver.analysis.Correlation: Convert a covariance matrix to a correlation matrix.
fromCovariance(CLGaussian, State...) - Static method in class com.bayesserver.analysis.Correlation
fromCovariance(CLGaussian, StateContext...) - Static method in class com.bayesserver.analysis.Correlation
FrontDoorCriterion - Class in com.bayesserver.causal: Uses the 'Front-door Criterion' to identify any sets of valid front-door nodes, that if found can be used to estimate the causal effect using theFrontDoorInference.
FrontDoorCriterion(Network) - Constructor for class com.bayesserver.causal.FrontDoorCriterion: Initializes a new instance of the FrontDoorCriterion class.
FrontDoorCriterionOptions - Class in com.bayesserver.causal: Options for FrontDoorCriterion.
FrontDoorCriterionOptions() - Constructor for class com.bayesserver.causal.FrontDoorCriterionOptions
FrontDoorCriterionOutput - Class in com.bayesserver.causal: The output from the Front-door criterion, including any sets of 'front-door nodes' identified.
FrontDoorInference - Class in com.bayesserver.causal: Estimates the causal effect, using the 'Front-door Adjustment' formula to avoid confounding bias.
FrontDoorInference(Network) - Constructor for class com.bayesserver.causal.FrontDoorInference: Initializes a new instance of the FrontDoorInference class.
FrontDoorInferenceFactory - Class in com.bayesserver.causal: Uses the factory design pattern to create inference related objects for the Front-door adjustment algorithm.
FrontDoorInferenceFactory() - Constructor for class com.bayesserver.causal.FrontDoorInferenceFactory
FrontDoorQueryOptions - Class in com.bayesserver.causal: Options for FrontDoorInference
FrontDoorQueryOptions() - Constructor for class com.bayesserver.causal.FrontDoorQueryOptions
FrontDoorQueryOutput - Class in com.bayesserver.causal: Returns any information, in addition to the distributions, that is requested from a query.
FrontDoorQueryOutput() - Constructor for class com.bayesserver.causal.FrontDoorQueryOutput: Initializes a new instance of the FrontDoorQueryOutput class.
FrontDoorSet - Class in com.bayesserver.causal: Front-door nodes used by the front-door adjustment.
FrontDoorSet(FrontDoorSetNode...) - Constructor for class com.bayesserver.causal.FrontDoorSet: Initializes a new instance of the FrontDoorSet class.
FrontDoorSet(List<FrontDoorSetNode>) - Constructor for class com.bayesserver.causal.FrontDoorSet: Initializes a new instance of the FrontDoorSet class.
FrontDoorSetNode - Class in com.bayesserver.causal: Represents a front-door node used by the front-door adjustment, and can be identified by the front-door criterion.
FrontDoorSetNode(Node) - Constructor for class com.bayesserver.causal.FrontDoorSetNode: Initializes a new instance of the FrontDoorSetNode class.
FrontDoorSetNode(Node, Integer) - Constructor for class com.bayesserver.causal.FrontDoorSetNode: Initializes a new instance of the FrontDoorSetNode class.
FrontDoorValidationOptions - Class in com.bayesserver.causal: Options for Front-door Criterion validation, which can be used to test whether the front-door nodes are valid and the pair of associated 'adjustment sets' are also valid..
FrontDoorValidationOptions(FrontDoorSet, AdjustmentSet, AdjustmentSet) - Constructor for class com.bayesserver.causal.FrontDoorValidationOptions: Initializes a new instance of the FrontDoorValidationOptions class.
FULLY_INSTANTIATED - com.bayesserver.analysis.DSeparationCategory: The test node has hard evidence set on all of its variables.
FUNCTION - com.bayesserver.VariableKind: A function variable, whose expression is evaluated at query time, and can be based on other queries/functions.
FUNCTION - com.bayesserver.VariableValueType: A function variable that generates a value during a call to Query on an inference engine, and is the result of a function evaluation.
FunctionException - Exception in com.bayesserver.inference: Exception raised during the evaluation of a function expression.
FunctionException() - Constructor for exception com.bayesserver.inference.FunctionException: Initializes a new instance of the FunctionException class.
FunctionException(String) - Constructor for exception com.bayesserver.inference.FunctionException: Initializes a new instance of the FunctionException class with a specified error message.
FunctionException(String, Throwable) - Constructor for exception com.bayesserver.inference.FunctionException: Initializes a new instance of the FunctionException class with a specified error message and a reference to the inner exception that is the cause of this exception.
FunctionException(Throwable) - Constructor for exception com.bayesserver.inference.FunctionException: Initializes a new instance of the FunctionException class with a reference to the inner exception that is the cause of this exception.
FunctionVariableExpression - Class in com.bayesserver: An expression that can be used in a function node/variable.
FunctionVariableExpression(String, ExpressionReturnType) - Constructor for class com.bayesserver.FunctionVariableExpression: Creates a new function node expression.

G

generate(DataReaderCommand, List<VariableDefinition>, VariableGeneratorOptions) - Static method in class com.bayesserver.data.discovery.VariableGenerator: Generates variables from a data source.
GENERATE - com.bayesserver.ExpressionDistribution: Generate the distribution, raising an error if invalid.
GENERATE_IF_VALID - com.bayesserver.ExpressionDistribution: Generate the distribution if a valid expression.
GeneticOptimizer - Class in com.bayesserver.optimization: A genetic algorithm optimizer.
GeneticOptimizer() - Constructor for class com.bayesserver.optimization.GeneticOptimizer
GeneticOptimizerOptions - Class in com.bayesserver.optimization: Options governing the behaviour of the com.bayesserver.optimization.genetic.GeneticOptimizer algorithm.
GeneticOptimizerOptions() - Constructor for class com.bayesserver.optimization.GeneticOptimizerOptions
GeneticOptimizerOutput - Class in com.bayesserver.optimization: Contains the results from the genetic optimization algorithm.
GeneticOptimizerProgressInfo - Class in com.bayesserver.optimization: Contains progress information sent from the genetic optimization algorithm.
GeneticOptionsBase - Class in com.bayesserver.optimization: Base class for common Genetic algorithm options.
GeneticSimplification - Class in com.bayesserver.optimization: An algorithm that attempts to simply the evidence found by an optimizer.
GeneticSimplification() - Constructor for class com.bayesserver.optimization.GeneticSimplification
GeneticSimplificationOptions - Class in com.bayesserver.optimization: Options for the genetic simplifcation algorithm.
GeneticSimplificationOptions() - Constructor for class com.bayesserver.optimization.GeneticSimplificationOptions
GeneticSimplificationOutput - Class in com.bayesserver.optimization: Contains the results from the genetic simplifcation algorithm.
GeneticTerminationOptions - Class in com.bayesserver.optimization: Termination options for the genetic optimization algorithm.
GeneticTerminationOptions() - Constructor for class com.bayesserver.optimization.GeneticTerminationOptions
get(int) - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
get(int) - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
get(int) - Method in class com.bayesserver.analysis.DSeparationTestResultCollection
get(int) - Method in class com.bayesserver.CustomPropertyCollection
get(int) - Method in class com.bayesserver.data.DataColumnCollection: Gets the DataColumn at the given index.
get(int) - Method in class com.bayesserver.data.DataRow: Gets the value at the specified index.
get(int) - Method in class com.bayesserver.data.DataRowCollection: Gets the row at the given index.
get(int) - Method in class com.bayesserver.data.sampling.ExcludedVariables
get(int) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
get(int) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
get(int) - Method in class com.bayesserver.inference.EliminationDefinitionCollection
get(int) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
get(int) - Method in class com.bayesserver.NetworkLinkCollection: Gets the Link object at the specified index.
get(int) - Method in class com.bayesserver.NetworkNodeCollection: Gets the Node object at the specified index.
get(int) - Method in class com.bayesserver.NetworkNodeGroupCollection
get(int) - Method in class com.bayesserver.NetworkVariableCollection: Gets the Variable object at the specified index.
get(int) - Method in class com.bayesserver.NodeDistributionExpressions: Gets a distribution expression at a particular temporal order.
get(int) - Method in class com.bayesserver.NodeDistributions: Gets a distribution at a particular temporal order.
get(int) - Method in class com.bayesserver.NodeGroupCollection: Gets the group at the specified index.
get(int) - Method in class com.bayesserver.NodeLinkCollection
get(int) - Method in class com.bayesserver.NodeVariableCollection: Gets the Variable object at the specified index.
get(int) - Method in class com.bayesserver.StateCollection: Gets the State at the specified index.
get(int) - Method in class com.bayesserver.Table: Gets the Table value at the specified index into the 1-dimensional array.
get(int) - Method in class com.bayesserver.TableAccessor: Gets the underlying Table value, specified i.
get(int) - Method in class com.bayesserver.VariableContextCollection: Gets the Variable object at the specified index.
get(int) - Method in class com.bayesserver.VariableMap: Maps between the custom order and the sorted collection.
get(int[]) - Method in class com.bayesserver.TableAccessor: Gets the underlying Table value, using states corresponding to the order of variables in the TableAccessor.
get(Node) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the hard evidence value for a particular node's variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(Node) - Method in interface com.bayesserver.inference.Evidence: Gets the hard evidence value for a particular node's variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributionExpressions: Gets a distribution expression with particular properties, such as temporal order.
get(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions: Gets a distribution with particular properties, such as temporal order.
get(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions: Gets a distribution expression with particular properties, such as temporal order.
get(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions: Gets a distribution with particular properties, such as temporal order.
get(NodeDistributionKey, NodeDistributionKind, ExpressionDistribution) - Method in class com.bayesserver.NodeDistributionExpressions: Gets a distribution expression with particular properties, such as temporal order.
get(NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions: Gets a particular kind of distribution expression on the node.
get(NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions: Gets a particular kind of distribution on the node.
get(Node, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the evidence for a node's single temporal variable.
get(Node, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence: Gets the evidence for a node's single temporal variable.
get(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the evidence for a node with a single variable at the specified time.
get(Node, Integer) - Method in interface com.bayesserver.inference.Evidence: Gets the evidence for a node with a single variable at the specified time.
get(State...) - Method in class com.bayesserver.Table: Gets the table value corresponding to the given states.
get(StateContext...) - Method in class com.bayesserver.Table: Gets the table value corresponding to the given states and associated times.
get(Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the hard evidence for a discrete variable or continuous variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(Variable) - Method in interface com.bayesserver.inference.Evidence: Gets the hard evidence for a discrete variable or continuous variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
get(Variable, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the evidence for a temporal variable.
get(Variable, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence: Gets the evidence for a temporal variable.
get(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the evidence for a discrete variable at the specified time.
get(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Gets the evidence for a discrete variable at the specified time.
get(String) - Method in class com.bayesserver.CustomPropertyCollection: Gets the CustomProperty with the specified name, from the collection, or returns null if not found.
get(String) - Method in class com.bayesserver.data.DataColumnCollection: Gets the column with the specified name, or null if the name if not found.
get(String) - Method in class com.bayesserver.NetworkNodeCollection: Performs a case sensitive lookup.
get(String) - Method in class com.bayesserver.NetworkNodeGroupCollection: Gets the NodeGroup with the specified name, from the collection, or returns null if not found.
get(String) - Method in class com.bayesserver.NetworkVariableCollection: Performs a case sensitive lookup.
get(String) - Method in class com.bayesserver.NodeVariableCollection: Performs a case sensitive lookup.
get(String) - Method in class com.bayesserver.StateCollection: Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.NetworkNodeCollection: Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.NetworkVariableCollection: Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.NodeVariableCollection: Performs a case sensitive lookup.
get(String, boolean) - Method in class com.bayesserver.StateCollection: Performs a case sensitive lookup.
getA() - Method in class com.bayesserver.learning.structure.LinkConstraint: Gets the first node involved in the constraint.
getAccuracy() - Method in class com.bayesserver.analysis.ConfusionMatrix: Gets the overall accuracy of the predictions, which is simply the ConfusionMatrix.getCorrectCount() divided by the ConfusionMatrix.getTotalCount().
getActual() - Method in class com.bayesserver.analysis.LiftChart: Gets the name of the data column containing the actual classification.
getAddNodeGroups() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets a value which determines whether network node groups are added for each group in a level.
getAdjustmentSet() - Method in class com.bayesserver.causal.BackdoorValidationOptions: Gets the adjustment set to be validated.
getAdjustmentSet() - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions: Gets the adjustment set, which must include all nodes that are causes of either treatments (X) or outcomes (Y) or both, except those with evidence set.
getAdjustmentSet() - Method in class com.bayesserver.causal.DisjunctiveCauseValidationOptions: Gets the adjustment set to be validated.
getAdjustmentSetOverride() - Method in class com.bayesserver.causal.BackdoorQueryOptions: Gets an adjustment set to use during estimation, instead of the algorithm generating it automatically.
getAdjustmentSetXZ() - Method in class com.bayesserver.causal.FrontDoorValidationOptions: Gets the adjustment set for the adjustment between treatments (X) and front-door nodes (Z).
getAdjustmentSetXZOverride() - Method in class com.bayesserver.causal.FrontDoorQueryOptions: Gets the 'adjustment set' for adjusting between treatments (X) and front-door nodes (Z).
getAdjustmentSetZY() - Method in class com.bayesserver.causal.FrontDoorValidationOptions: Gets the adjustment set for the adjustment between front-door nodes (Z) and outcomes (Y).
getAdjustmentSetZYOverride() - Method in class com.bayesserver.causal.FrontDoorQueryOptions: Gets the 'adjustment set' for adjusting between the front-door nodes (Z) and the outcomes (Y).
getAllowMissing() - Method in class com.bayesserver.optimization.DesignVariable: Determines whether the optimizer can consider missing values (evidence not set) on this variable.
getAllowNullDistributions() - Method in class com.bayesserver.ValidationOptions: Determines whether validation should succeed even if the required distribution(s) have not been assigned to a node.
getAllowNullFunctions() - Method in class com.bayesserver.ValidationOptions: Determines whether validation should succeed even if a function has not been assigned to a functiomn variable.
getAlpha() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets Alpha from the sensitivity function.
getAlpha1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Alpha1 from the sensitivity function.
getAlpha2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Alpha2 from the sensitivity function.
getAnomalyScore() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput: Gets a value between [0, 1] with values closer to 0 being more likely to be anomalous.
getAssigned() - Method in class com.bayesserver.inference.CliqueDefinition: Identifies any original network distributions that are multiplied into the clique.
getAutoCommit() - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Gets the auto commit value to be set on each connection created.
getAutoDetectDiscreteLimit() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets the distinct value count, which when exceeded changes a variable from discrete to continuous.
getAutoReadTemporal() - Method in class com.bayesserver.data.DefaultEvidenceReader: Determines whether any temporal data is read automatically.
getB() - Method in class com.bayesserver.learning.structure.LinkConstraint: Gets the second node involved in the constraint.
getBaseEvidence() - Method in class com.bayesserver.causal.CausalInferenceBase: Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in interface com.bayesserver.inference.Inference: Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.LoopyBeliefInference: Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.RelevanceTreeInference: Optional evidence which can be used to calculate the lift of queries.
getBaseEvidence() - Method in class com.bayesserver.inference.VariableEliminationInference: Optional evidence which can be used to calculate the lift of queries.
getBaseline() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutput: Gets baseline log-likelihood values.
getBestToWorst() - Method in class com.bayesserver.analysis.ClusterCountOutput: A list of scores, sorted from best to worst.
getBeta() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets Beta from the sensitivity function.
getBeta1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Beta1 from the sensitivity function.
getBeta2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Beta2 from the sensitivity function.
getBIC() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the Bayesian Information Criterion (BIC) for the final learnt Network based on the learning data.
getBoolean(int) - Method in class com.bayesserver.data.DataReaderFiltered
getBoolean(int) - Method in interface com.bayesserver.data.DataRecord: Gets a boolean value for the specified column.
getBoolean(int) - Method in class com.bayesserver.data.DataTableReader
getBoolean(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets a boolean value for the specified column.
getBounds() - Method in class com.bayesserver.Node: Gets the size and location of the node.
getCalculateStatistics() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets a value indicating whether to calculate summary statistics in an extra iteration at the end of learning.
getCancel() - Method in interface com.bayesserver.Cancellation: When set to true attempts to cancel a long running operation.
getCancel() - Method in class com.bayesserver.DefaultCancellation: When set to true attempts to cancel a long running operation.
getCancellation() - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions: Gets of sets an instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets of sets an instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Allows cancellation of a query.
getCancellation() - Method in interface com.bayesserver.inference.QueryOptions: Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.TreeQueryOptions: Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Allows cancellation of a query.
getCancellation() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
getCancellation() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions: Used to cancel a long running operation.
getCanStop() - Method in interface com.bayesserver.Stop: When true, indicates that the algorithm supports early stopping.
getCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the number of cases (records) in the learning data.
getCaseId() - Method in class com.bayesserver.data.ReadInfo: The current case id.
getCaseIdColumn() - Method in class com.bayesserver.data.NestedDataReader: The name of the case identifier column, which links to the case table.
getCaseIdColumn() - Method in class com.bayesserver.data.ReaderOptions: The name of the case identifier column, if one is present.
getCaseIdColumn() - Method in class com.bayesserver.data.TemporalReaderOptions: The name of the temporal case identifier column, if one is present.
getCategory() - Method in class com.bayesserver.analysis.DSeparationTestResult: The test result.
getCausalEffectKind() - Method in class com.bayesserver.causal.BackdoorCriterionOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in class com.bayesserver.causal.BackdoorValidationOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in class com.bayesserver.causal.DisjunctiveCauseValidationOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in class com.bayesserver.causal.FrontDoorCriterionOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in class com.bayesserver.causal.FrontDoorValidationOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in interface com.bayesserver.causal.IdentificationOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in interface com.bayesserver.causal.ValidationOptions: The type of causal effect, such as Total or Direct.
getCausalEffectKind() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in interface com.bayesserver.inference.QueryOptions: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Gets the kind of effect to calculate.
getCausalEffectKind() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets the kind of causal effect to optimize.
getCausalEffectKind() - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets the kind of causal effect to optimize.
getCausalInferenceFactory() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalInferenceFactory() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalInferenceFactory() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalInferenceFactory() - Method in interface com.bayesserver.inference.QueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalInferenceFactory() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalInferenceFactory() - Method in class com.bayesserver.inference.TreeQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalInferenceFactory() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
getCausalObservability() - Method in class com.bayesserver.Node: The CausalObservability of the node.
getCausesOfTreatmentsOrOutcomes() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions: Gets a list of nodes which must include all causes of treatments (X) or causes of outcomes (Y) or causes of both.
getCausesOfTreatmentsOrOutcomes() - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions: Gets the list of all nodes that are either causes of treatments (X) or outcomes (Y) or both.
getCell(Comparable, Comparable) - Method in class com.bayesserver.analysis.ConfusionMatrix: Gets information about a cell in a confusion matrix.
getChild() - Method in class com.bayesserver.inference.CliqueDefinition: The child node in the junction tree (if any).
getChild() - Method in class com.bayesserver.inference.SepsetDefinition: The child clique in the junction tree.
getCleared() - Method in class com.bayesserver.data.DefaultReadOptions: Gets a value indicating whether the Evidence has been cleared prior to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions) being called.
getCleared() - Method in interface com.bayesserver.data.ReadOptions: Gets a value indicating whether the Evidence has been cleared prior to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions) being called.
getCliques() - Method in class com.bayesserver.inference.JunctionTreesDefinition: The cliques in the junction tree(s).
getClusterCount() - Method in class com.bayesserver.analysis.ClusterScore: The number of clusters used to generate this score.
getClusterVariableName() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the name of the cluster/latent node/variable created when more than 1 hidden state is detected.
getColumn() - Method in class com.bayesserver.data.VariableReference: Gets the name of the relevant column in the data source.
getColumnCount() - Method in class com.bayesserver.data.DataReaderFiltered
getColumnCount() - Method in interface com.bayesserver.data.DataRecord: Gets the number of columns (fields) in the data.
getColumnCount() - Method in class com.bayesserver.data.DataTableReader
getColumnCount() - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets the number of columns (fields) in the data.
getColumnIndex(String) - Method in class com.bayesserver.data.DataReaderFiltered
getColumnIndex(String) - Method in interface com.bayesserver.data.DataRecord: Gets the zero based column index for a column name.
getColumnIndex(String) - Method in class com.bayesserver.data.DataTableReader
getColumnIndex(String) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets the zero based column index for a column name.
getColumnName() - Method in class com.bayesserver.data.DataColumn
getColumnName() - Method in class com.bayesserver.data.discovery.DiscretizationColumn: Gets the name of the column of data to be discretized.
getColumnName(int) - Method in class com.bayesserver.data.DataReaderFiltered
getColumnName(int) - Method in interface com.bayesserver.data.DataRecord: Gets the name of the column at the specified index.
getColumnName(int) - Method in class com.bayesserver.data.DataTableReader
getColumnName(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets the name of the column at the specified index.
getColumns() - Method in class com.bayesserver.data.DataTable: Gets the columns in the table.
getColumnType(int) - Method in class com.bayesserver.data.DataReaderFiltered
getColumnType(int) - Method in interface com.bayesserver.data.DataRecord: Get the data type for the specified column.
getColumnType(int) - Method in class com.bayesserver.data.DataTableReader
getColumnType(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Get the data type for the specified column.
getColumnValueType() - Method in class com.bayesserver.data.VariableReference: Gets the type of value in the bound data column.
getComparison() - Method in class com.bayesserver.inference.QueryDistribution: Gets a value indicating whether queried values should be adjusted to show how they compare to the same query with no evidence, or base evidence.
getConfiguration() - Method in interface com.bayesserver.Distributer: Gets configuration name value pairs which must be made available to the distributed workers.
getConflict() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.causal.CausalQueryOutputBase: Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput: Gets the conflict measure.
getConflict() - Method in interface com.bayesserver.inference.QueryOptions: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in interface com.bayesserver.inference.QueryOutput: Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput: Gets the conflict measure.
getConflict() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Gets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getConflict() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput: Gets the conflict measure.
getContinuous() - Method in class com.bayesserver.learning.parameters.Priors: Gets the amount continuous distributions are adjusted during learning.
getContinuousTargetInterval() - Method in class com.bayesserver.analysis.AutoInsightOutput: Gets the target interval (if any).
getConverged() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets a value indicating whether this parameter learning converged.
getConvergenceMethod() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the method used to determine convergence of the learning algorithm.
getCorrectCount() - Method in class com.bayesserver.analysis.ConfusionMatrix: Gets the total number of correct predictions.
getCounts() - Method in class com.bayesserver.data.discovery.VariableInfo: Gets counts such as missing and non-missing data for the variable.
getCovariance(int, int, int) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getCovariance(VariableContext, VariableContext, State...) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(VariableContext, VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(VariableContext, VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Variable) - Method in class com.bayesserver.CLGaussian: Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
getCovariance(Variable, Variable, State...) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Variable, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Variable, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Integer, Variable, Integer) - Method in class com.bayesserver.CLGaussian: Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
getCovariance(Variable, Integer, Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Integer, Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCovariance(Variable, Integer, Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
getCrossoverProbability() - Method in class com.bayesserver.optimization.GeneticOptionsBase: The probability of parents being crossed.
getCustomProperties() - Method in class com.bayesserver.Link: Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.Network: Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.Node: Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.NodeGroup: Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.State: Gets custom properties associated with this instance.
getCustomProperties() - Method in class com.bayesserver.Variable: Gets custom properties associated with this instance.
getDataColumn() - Method in class com.bayesserver.data.discovery.VariableDefinition: The name of the data column, containing the data used to generate the new variable.
getDataProgress() - Method in class com.bayesserver.data.DefaultEvidenceReader: Gets the instance used to report progress on the number of cases read.
getDataProgress() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Reports progress on the number of cases read.
getDataProgressInterval() - Method in class com.bayesserver.data.DefaultEvidenceReader: Gets a value which determines how often progress events are raised.
getDataReader() - Method in class com.bayesserver.data.NestedDataReader: Gets the nested data reader.
getDataType() - Method in class com.bayesserver.data.DataColumn: Gets the type of data the column contains.
getDbn() - Method in class com.bayesserver.UnrollOutput: Gets the Dynamic Bayesian network before it was unrolled.
getDbnNode(Node) - Method in class com.bayesserver.UnrollOutput: Maps from a node in the unrolled network to the corresponding node in the original Dynamic Bayesian network.
getDbnVariable(Variable) - Method in class com.bayesserver.UnrollOutput: Maps from a variable in the unrolled network to the corresponding variable in the original Dynamic Bayesian network.
getDecisionAlgorithm() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in interface com.bayesserver.inference.QueryOptions: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Gets the algorithm to use when a network contains Decision nodes.
getDecisionAlgorithm() - Method in class com.bayesserver.learning.parameters.OnlineLearningOptions: Gets the algorithm to use for adaption of decision graphs.
getDecisionPostProcessing() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the post processing method for decision nodes.
getDecomposedNetwork() - Method in class com.bayesserver.DecomposeOutput: Gets the network, which is the decomposed equivalent of the original network.
getDecomposedVariable(Variable) - Method in class com.bayesserver.DecomposeOutput: Maps a variable in the original network to the equivalent variable in the decomposed network.
getDefault() - Static method in class com.bayesserver.NodeDistributionKey: Gets a default instance, which is equivalent to constructing a new instance with the default constructor.
getDelta() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets Delta from the sensitivity function.
getDelta() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo: Gets the relative change in parameters used to determine convergence.
getDelta1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Delta1 from the sensitivity function.
getDelta2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Delta2 from the sensitivity function.
getDescription() - Method in class com.bayesserver.CustomProperty: An optional description for the custom property.
getDescription() - Method in class com.bayesserver.Link: Optional description for the link.
getDescription() - Method in class com.bayesserver.Network: An optional description for the Bayesian network.
getDescription() - Method in class com.bayesserver.Node: An optional description for the node.
getDescription() - Method in class com.bayesserver.NodeGroup: An optional description for the custom property.
getDescription() - Method in class com.bayesserver.State: Gets an optional description for the state.
getDescription() - Method in class com.bayesserver.Variable: An optional description for the variable.
getDesignStates() - Method in class com.bayesserver.optimization.DesignVariable: Gets the design states, one for each state in the variable.
getDetectIntegralFloats() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets a value, which when true tests floating point column data to see if the data is an integral type, which would then become a candidate to be a discrete variable when VariableValueType is not specified.
getDifference() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the difference between the probability of this state given the target state and the probability of this state excluding the target state.
getDiscrete() - Method in class com.bayesserver.learning.parameters.Priors: Gets the amount distributions containing discrete variables are adjusted during learning.
getDiscretePriorMethod() - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Gets the type of discrete prior to use for this distribution.
getDiscretePriorMethod() - Method in class com.bayesserver.learning.parameters.Priors: The default discrete prior to use for discrete distributions during parameter learning.
getDiscretizationMethod() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets the method (algorithm) to use for discretization, if any.
getDiscretizationOptions() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets options that specify how continuous data should be discretized, if DiscretizationMethod is not DiscretizationMethod.NONE.
getDistance() - Method in class com.bayesserver.inference.QueryDistribution: The distance between this query calculated with base evidence or no evidence, and when calculated with evidence.
getDistribution() - Method in class com.bayesserver.inference.QueryDistribution: Gets the distribution to query.
getDistribution() - Method in class com.bayesserver.Node: Returns the distribution currently associated with the Node.
getDistribution() - Method in class com.bayesserver.NodeDistributions.DistributionOrder: Gets the distribution.
getDistributionMonitoring() - Method in interface com.bayesserver.learning.parameters.ParameterLearningProgress: Gets information about the current state of distributions being monitored.
getDistributionOptions() - Method in class com.bayesserver.Node: Options that apply to all distributions of this instance.
getDistributions() - Method in class com.bayesserver.Node: Returns the distributions associated with this instance with NodeDistributionKind = Probability.
getDouble(int) - Method in class com.bayesserver.data.DataReaderFiltered
getDouble(int) - Method in interface com.bayesserver.data.DataRecord: Gets a double value for the specified column.
getDouble(int) - Method in class com.bayesserver.data.DataTableReader
getDouble(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets a double value for the specified column.
getEliminationOrder() - Method in class com.bayesserver.inference.JunctionTreesDefinition: The order in which nodes would be eliminated to create the junction tree(s).
getEliminations() - Method in class com.bayesserver.inference.CliqueDefinition: Identifies any nodes that are eliminated at this stage of inference.
getEmptyStringAction() - Method in class com.bayesserver.data.discovery.VariableDefinition: Determines the action to take if an empty string is encountered.
getEmptyStringAction() - Method in class com.bayesserver.data.VariableReference: Determines the action to take if an empty string is encountered.
getEnsureTestWithoutCluster() - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets a value which indicates whether a test must be included which excludes the cluster variable altogether.
getEntropyX() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets the entropy for X.
getEntropyY() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets the entropy for Y.
getEnumerateAllMissing() - Method in class com.bayesserver.analysis.CombinationOptions: Gets a value which indicates whether the combination where all states are null/missing should be included in the enumeration.
getEnumerateMissing() - Method in class com.bayesserver.analysis.CombinationOptions: Gets a value which indicates whether null/missing values should be enumerated in addition to each state.
getEvidence() - Method in class com.bayesserver.causal.CausalInferenceBase: Represents the evidence, or case data (e.g.
getEvidence() - Method in interface com.bayesserver.inference.Inference: Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.LoopyBeliefInference: Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.RelevanceTreeInference: Represents the evidence, or case data (e.g.
getEvidence() - Method in class com.bayesserver.inference.VariableEliminationInference: Gets the evidence (case data, e.g.
getEvidence() - Method in class com.bayesserver.learning.parameters.OnlineLearning: Gets the evidence used internally.
getEvidence() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput: The evidence required to produce the optimized objective value.
getEvidence() - Method in class com.bayesserver.optimization.GeneticOptimizerProgressInfo: Gets the evidence for the objective value.
getEvidence() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput: The evidence required to produce the optimized objective value.
getEvidence() - Method in interface com.bayesserver.optimization.OptimizerOutput: The evidence required to produce the optimized objective value.
getEvidence() - Method in interface com.bayesserver.optimization.OptimizerProgressInfo: Gets the evidence for the objective value.
getEvidenceFlags() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets a list of values each of which indicate which of the evidence being analyzed is set.
getEvidenceFlags() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutputItem: Gets a list of values each of which indicate which of the evidence being analyzed is set.
getEvidenceKind() - Method in class com.bayesserver.optimization.DesignVariable: Determines whether the optimizer uses hard or soft/virtual evidence for this variable.
getEvidenceReader() - Method in class com.bayesserver.data.EvidenceReaderEventArgs: Gets the reader created by a reader command.
getEvidenceToSimplify() - Method in class com.bayesserver.optimization.GeneticSimplificationOptions: The evidence from a previous optimization.
getEvidenceType() - Method in class com.bayesserver.inference.EvidenceTypes: Gets the EvidenceType.
getEvidenceType(Node) - Method in class com.bayesserver.inference.DefaultEvidence: Returns the type of evidence currently set for a node with a single variable.
getEvidenceType(Node) - Method in interface com.bayesserver.inference.Evidence: Returns the type of evidence currently set for a node with a single variable.
getEvidenceType(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Returns the type of evidence currently set for a node with a single variable at a given time.
getEvidenceType(Node, Integer) - Method in interface com.bayesserver.inference.Evidence: Returns the type of evidence currently set for a node with a single variable at a given time.
getEvidenceType(Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Returns the type of evidence currently set for a variable (if any).
getEvidenceType(Variable) - Method in interface com.bayesserver.inference.Evidence: Returns the type of evidence currently set for a variable (if any).
getEvidenceType(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Returns the type of evidence currently set for a variable at a given time.
getEvidenceType(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Returns the type of evidence currently set for a variable at a given time.
getEvidenceTypes(Node) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Node) - Method in interface com.bayesserver.inference.Evidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Node, Integer) - Method in interface com.bayesserver.inference.Evidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Variable) - Method in interface com.bayesserver.inference.Evidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getEvidenceTypes(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
getExcludeNullDistributions() - Method in class com.bayesserver.ParameterCountOptions: Gets a value indicating whether null distributions are excluded from the parameter count.
getExpression() - Method in class com.bayesserver.NodeDistributionExpressions.DistributionExpressionOrder: Gets the expression.
getExpressionAlias() - Method in class com.bayesserver.Variable: Gets a c-style name for a variable that can be used as an alias in expressions.
getExpressions() - Method in class com.bayesserver.NodeDistributions: Gets any expressions associated with a node, that are used to generate distributions.
getFactory() - Method in class com.bayesserver.analysis.ImpactOptions: Gets the inference factory which is used to create inference engines during an impact analysis.
getFactory() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Gets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
getFailureMode() - Method in class com.bayesserver.learning.structure.LinkConstraint: Gets the action to take when this link constraint is violated.
getFetchSize() - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Gets the fetch size to be set on each statement created.
getFloat(int) - Method in class com.bayesserver.data.DataReaderFiltered
getFloat(int) - Method in interface com.bayesserver.data.DataRecord: Gets a float value for the specified column.
getFloat(int) - Method in class com.bayesserver.data.DataTableReader
getFloat(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets a float value for the specified column.
getFrom() - Method in class com.bayesserver.Link: The parent node of the directed link.
getFrontDoorNodes() - Method in class com.bayesserver.causal.FrontDoorValidationOptions: Gets the front-door nodes to use during validation.
getFrontDoorNodesOverride() - Method in class com.bayesserver.causal.FrontDoorQueryOptions: Gets the set of front-door nodes (Z) used by the front-door adjustment.
getFunction() - Method in class com.bayesserver.Variable: Gets an expression, which is evaluated during a query, and can be based on other queries and expressions.
getFunctionOutput() - Method in class com.bayesserver.inference.QueryFunction: Gets the function to evaluate.
getGamma() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets Gamma from the sensitivity function.
getGamma1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Gamma1 from the sensitivity function.
getGamma2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets Gamma2 from the sensitivity function.
getGap() - Method in class com.bayesserver.DecomposeOptions: The gap between decomposed nodes, used when laying out new nodes.
getGroups() - Method in class com.bayesserver.Node: Gets the groups this node belongs to.
getHasTemporalReader() - Method in class com.bayesserver.data.DefaultDataReader: Gets a value indicating whether the reader includes temporal data.
getHasZeroIntercepts() - Method in class com.bayesserver.NodeDistributionOptions: Determines whether CLGaussian intercept terms are fixed to zero.
getHeadTail() - Method in class com.bayesserver.VariableContext: Specifies whether the variable is marked as Head or Tail.
getHeight() - Method in class com.bayesserver.Bounds: Gets the height of the element.
getHypothesis() - Method in class com.bayesserver.analysis.ImpactOutput: Gets output information for the hypothesis variable/state.
getHypothesisImprovement() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput: Gets the improvement between the hypothesis statistic when we do not have knowledge about this test variable and when we do.
getHypothesisStatistic() - Method in class com.bayesserver.analysis.ValueOfInformationOutput: Gets the statistic associated with the hypothesis before any test variables have evidence set.
getHypothesisStatistic() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput: Gets the statistic for the hypothesis given knowledge on this test variable.
getIdeal() - Method in class com.bayesserver.analysis.LiftChart: Gets the population probability value at which the target reaches 100 %.
getIncludeGlobalCovariance() - Method in class com.bayesserver.learning.parameters.Priors: When Gaussian distributions are adjusted according to the Priors.getContinuous() prior, this property determines whether the global covariance should be included in the adjustment, as well as the global variance.
getInconsistentEvidenceMode() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Determines when an InconsistentEvidenceException is raised.
getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Determines when an InconsistentEvidenceException is raised.
getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Determines when an InconsistentEvidenceException is raised.
getInconsistentEvidenceMode() - Method in interface com.bayesserver.inference.QueryOptions: Determines when an InconsistentEvidenceException is raised.
getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Determines when an InconsistentEvidenceException is raised.
getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.TreeQueryOptions: Determines when an InconsistentEvidenceException is raised.
getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Determines when an InconsistentEvidenceException is raised.
getIndependence() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets options controlling how the independence tests are carried out.
getIndex() - Method in class com.bayesserver.Link: The Index of this instance in the collection of links belonging to a network, or -1 if the link does not belong to a network.
getIndex() - Method in class com.bayesserver.Node: The Index of this instance in the collection of nodes belonging to a network, or -1 if the node does not belong to a network.
getIndex() - Method in class com.bayesserver.State: Gets the index of the state in a variable's Variable.getStates() collection.
getIndex() - Method in class com.bayesserver.Table.MaxValue
getIndex() - Method in class com.bayesserver.Variable: The Index of this instance in the collection of variables belonging to a network, or -1 if the variable does not belong to a node and hence a network.
getInference() - Method in interface com.bayesserver.inference.QueryLifecycleBegin: The current inference engine.
getInference() - Method in class com.bayesserver.inference.QueryLifecycleBeginBase: The current inference engine.
getInference() - Method in interface com.bayesserver.inference.QueryLifecycleEnd: The current inference engine.
getInference() - Method in class com.bayesserver.inference.QueryLifecycleEndBase: The current inference engine.
getInferenceFactory() - Method in class com.bayesserver.analysis.AssociationOptions: Gets the inference factory used for link strength calculations.
getInferenceFactory() - Method in class com.bayesserver.analysis.AutoInsightOptions: Gets the inference factory used for link strength calculations.
getInferenceFactory() - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets the factory which is used to create inference engines during the cluster count tests.
getInferenceFactory() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions: Gets the factory which is used to create inference engines during the in-sample anomaly detection process.
getInferenceFactory() - Method in class com.bayesserver.causal.AbductionOptions: Used to create an inference engine, to determine the values for the characterstic variables.
getInferenceFactory() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the inference factory used during scoring.
getInferenceFactory() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the inference factory used during scoring.
getInferenceFactory() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets the inference factory used during scoring.
getInferenceFactory() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Used to create one or more inference engines, used by the algorithm to determine the fitness of possible solutions.
getInferenceFactory() - Method in interface com.bayesserver.optimization.OptimizerOptions: Creates one or more inference engines used by the optimization algorithm.
getInfiniteExtremes() - Method in class com.bayesserver.data.discovery.DiscretizationOptions: Gets a value indicating whether the first and last intervals extend to negative and positive infinity respectively.
getInitialization() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Options for initialization.
getInitialize() - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Gets a flag indicating whether the distribution should be initialized.
getInitializeDistributions() - Method in class com.bayesserver.learning.parameters.InitializationOptions: Indicates whether or not to initialize distributions by default.
getInnerMessage() - Method in class com.bayesserver.data.discovery.VariableGeneratorProgressInfo: Gets an inner progress message.
getInt(int) - Method in class com.bayesserver.data.DataReaderFiltered
getInt(int) - Method in interface com.bayesserver.data.DataRecord: Gets an integer value for the specified column.
getInt(int) - Method in class com.bayesserver.data.DataTableReader
getInt(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets an integer value for the specified column.
getInterval() - Method in class com.bayesserver.analysis.HistogramDensityItem: The histogram density interval.
getInterval() - Method in class com.bayesserver.analysis.ParameterTuningOneWay: Gets the interval for the parameter which satisfies the constraint used in parameter tuning.
getIntervals() - Method in class com.bayesserver.data.discovery.DiscretizationInfo: Gets the intervals generated by a discretization algorithm for a column of data.
getInterventionColumn() - Method in class com.bayesserver.data.VariableReference: Gets the optional name of a column in the data source that identifies whether this is an intervention (Do evidence) or not.
getInterventionType() - Method in class com.bayesserver.inference.EvidenceTypes: Gets the InterventionType.
getInterventionType() - Method in class com.bayesserver.optimization.DesignVariable: Determines the evidence intervention type for this variable.
getIsApproximate() - Method in class com.bayesserver.analysis.AutoInsightOutput: Gets a value which when true indicates that the auto-insight calculations were approximated using sampling.
getIsConstant() - Method in class com.bayesserver.data.discovery.VariableInfo: Gets a value which when true indicates that the variable has a constant value.
getIsEnabled() - Method in class com.bayesserver.inference.QueryDistribution: Gets a value indicating whether the distribution should be queried.
getIsEnabled() - Method in class com.bayesserver.inference.QueryFunction: Gets a value indicating whether the function should be evaluated.
getIsImpliedEvidenceEnabled() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets a value indicating whether to detect implied evidence during the calculation.
getIsInternal() - Method in class com.bayesserver.Network: For internal use only.
getIsProper() - Method in class com.bayesserver.causal.BackdoorGraphOptions: Gets a value which determines whether a 'proper Backdoor graph' is constructed.
getIsReadOnly() - Method in class com.bayesserver.StateCollection: Gets a value indicating whether or not the collection is read-only.
getIsValid() - Static method in class com.bayesserver.License: Gets a value indicating whether a license has been successfully validated or not.
getItems() - Method in class com.bayesserver.analysis.HistogramDensity: The collection of intervals and their statistics making up the histogram density.
getItems() - Method in class com.bayesserver.analysis.ImpactOutput: Gets the output for each combination.
getItems() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutput: Gets the output for each combination.
getItems() - Method in class com.bayesserver.causal.EffectsAnalysisOutput: A result for each treatment value.
getIterationCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the number of iterations performed during learning.
getIterationCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo: Gets the current iteration count.
getIterations() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput: Gets the number of iterations performed.
getJSDivergence() - Method in class com.bayesserver.analysis.AutoInsightOptions: Gets a value which determines the type of Jensen Shannon divergence calculations to perform, if any.
getJSDivergenceBits() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput: Gets the Jensen Shannon divergence for the test distribution, measured in BITS.
getJunctionTree() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets a value indicating whether or not to generate a junction tree definition.
getJunctionTrees() - Method in class com.bayesserver.inference.TreeQueryOutput: Gets the junction tree definition, if requested.
getKeepEvidenceNotAnalyzed() - Method in class com.bayesserver.analysis.ImpactOptions: Gets a value which when true retains evidence not being analysed, or when false ignores it.
getKeepEvidenceNotAnalyzed() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Gets a value which when true retains evidence not being analysed, or when false ignores it.
getKey() - Method in class com.bayesserver.analysis.ParameterReference: Gets the of the node's distribution being referenced.
getKey() - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Gets the order/related node of the distribution.
getKeys() - Method in class com.bayesserver.NodeDistributionExpressions: Gets the collection of node distribution keys that require distributions.
getKeys() - Method in class com.bayesserver.NodeDistributions: Gets the collection of node distribution keys that require distributions.
getKind() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput: Gets the type of Value of information statistic calculated.
getKind() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets the VariableKind for the new variable.
getKind() - Method in class com.bayesserver.optimization.Objective: Gets the kind of optimization to carry out.
getKind() - Method in class com.bayesserver.Variable: Gets the kind of variable, such as Probability, Decision, Utility or Function.
getKLDivergence() - Method in class com.bayesserver.analysis.AutoInsightOptions: Gets a value which determines the type of KL divergence calculations to perform, if any.
getKLDivergence() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput: Gets the Kullback-Leibler divergence for the test distribution, and tells us how much the test variance changes with the hypothesis.
getKLDivergenceFromNone() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the Kullback-Leibler divergence D(P||Q) from the hypothesis query without evidence to analyze set (Q) to the current combination (P).
getKLDivergenceToAll() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the Kullback-Leibler divergence D(P||Q) from the hypothesis query with the current subset of evidence (Q) to all evidence to analyze set (P).
getLift() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the ratio of the probability of this state given the target state over the probability of this state excluding the target state.
getLink() - Method in class com.bayesserver.learning.structure.ChowLiuLinkOutput: Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.ClusteringLinkOutput: Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.HierarchicalLinkOutput: Gets the new link.
getLink() - Method in interface com.bayesserver.learning.structure.LinkOutput: Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.PCLinkOutput: Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.SearchLinkOutput: Gets the new link.
getLink() - Method in class com.bayesserver.learning.structure.TANLinkOutput: Gets the new link.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkConstraints() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets any link constraints to use during structural learning.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOutput: Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOutput: Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOutput: Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOutput: Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOutput: Gets information about any new links added during the learning process.
getLinkOutputs() - Method in interface com.bayesserver.learning.structure.StructuralLearningOutput: Gets information about any new links added during the learning process.
getLinkOutputs() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOutput: Gets information about any new links added during the learning process.
getLinks() - Method in class com.bayesserver.Network: The collection of links in the Bayesian network.
getLinks() - Method in class com.bayesserver.Node: Collection of both incoming and outgoing links (parent and child nodes).
getLinksIn() - Method in class com.bayesserver.Node: Collection of incoming links (linking to parent nodes).
getLinksOut() - Method in class com.bayesserver.Node: Collection of outgoing links (linking to child nodes).
getLocked() - Method in class com.bayesserver.CLGaussian: Locks or unlocks a distribution.
getLocked() - Method in interface com.bayesserver.Distribution: Locks or unlocks a distribution.
getLocked() - Method in class com.bayesserver.Table: Locks or unlocks a distribution.
getLogarithmBase() - Method in class com.bayesserver.analysis.ValueOfInformationOptions: The logarithm base to use when calculating ValueOfInformation.
getLogLikelihood() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutputItem: Gets the log-likelihood for this output item evidence.
getLogLikelihood() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.causal.CausalQueryOutputBase: Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput: Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.QueryDistribution: The log-likelihood specific to the evidence used to calculate this query.
getLogLikelihood() - Method in interface com.bayesserver.inference.QueryOptions: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in interface com.bayesserver.inference.QueryOutput: Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput: Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Gets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getLogLikelihood() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput: Gets the log-likelihood value.
getLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the log likelihood of the learning data with the final learnt Network.
getLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo: Gets the current log likelihood value, if calculated
getLogLikelihoodAll() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisBaselineOutput: Gets the log-likelihood with all evidence to analyze set.
getLogLikelihoodNone() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisBaselineOutput: Gets the log-likelihood with no evidence to analyze set.
getLogWeight() - Method in class com.bayesserver.inference.DefaultEvidence: Gets the natural logarithm of Evidence.getWeight().
getLogWeight() - Method in interface com.bayesserver.inference.Evidence: Gets the natural logarithm of Evidence.getWeight().
getLong(int) - Method in class com.bayesserver.data.DataReaderFiltered
getLong(int) - Method in interface com.bayesserver.data.DataRecord: Gets a long value for the specified column.
getLong(int) - Method in class com.bayesserver.data.DataTableReader
getLong(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets a long value for the specified column.
getLowerBound() - Method in class com.bayesserver.optimization.DesignState: The minimum value allowed for this variable/state during the optimization process.
getMaxDepth() - Method in class com.bayesserver.TopologicalSortNodeInfo: Gets the maximum number of links from a root node to this node.
getMaxEvidenceSubsetSize() - Method in class com.bayesserver.analysis.ImpactOptions: Gets the maximum size of evidence subsets to consider.
getMaxEvidenceSubsetSize() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Gets the maximum size of evidence subsets to consider.
getMaximum() - Method in class com.bayesserver.Interval: Gets the maximum interval value.
getMaximumAdjustmentSets() - Method in class com.bayesserver.causal.BackdoorCriterionOptions: Limits the number of adjustment sets generated.
getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
getMaximumClusterCount() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the maximum number of clusters generated.
getMaximumClustersPerGroup() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the maximum number of clusters generated for each group.
getMaximumConcurrency() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the maximum number of inference engines used during learning.
getMaximumConcurrency() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets the maximum number of inference engines used during optimization.
getMaximumConcurrency() - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets the maximum number of inference engines used during optimization.
getMaximumConditional() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets the maximum number of conditional variables to consider during independence testing.
getMaximumEndPoint() - Method in class com.bayesserver.Interval: Gets the end point type for the maximum value of the interval.
getMaximumGroupsPerLevel() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the maximum number of groups created per level.
getMaximumIterations() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the maximum number of iterations that parameter learning will perform.
getMaximumIterations() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the maximum number of iterations used by parameter learning to score each configuration.
getMaximumIterations() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the maximum number of iterations used by parameter learning to score each configuration.
getMaximumIterations() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets the optional maximum number of iterations (moves) made during the search procedure.
getMaximumLevels() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the maximum number of levels generated by the algorithm.
getMaximumSupport() - Method in class com.bayesserver.learning.parameters.InitializationOptions: Limits the amount of support each distribution is given during initialization.
getMaximumTemporalOrder() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets the maximum order of temporal links that are considered during learning.
getMaxTemporalOrder() - Method in class com.bayesserver.NodeDistributionExpressions: Gets the current maximum temporal order.
getMaxTemporalOrder() - Method in class com.bayesserver.NodeDistributions: Gets the current maximum temporal order.
getMaxTime() - Method in class com.bayesserver.inference.DefaultEvidence: Gets the maximum time containing evidence.
getMaxTime() - Method in interface com.bayesserver.inference.Evidence: Gets the maximum time containing evidence.
getMaxTime(Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the maximum time containing evidence for a variable.
getMaxTime(Variable) - Method in interface com.bayesserver.inference.Evidence: Gets the maximum time containing evidence for a variable.
getMaxValue() - Method in class com.bayesserver.Table: Gets the maximum table value, and the index at which it occurs.
getMean() - Method in class com.bayesserver.statistics.IntervalStatistics: Gets the mean of the discretized variable.
getMean(int, int) - Method in class com.bayesserver.CLGaussian: Gets the mean of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getMean(Variable) - Method in class com.bayesserver.CLGaussian: Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
getMean(VariableContext, State...) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
getMean(Variable, State...) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
getMean(Variable, Integer) - Method in class com.bayesserver.CLGaussian: Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
getMean(Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
getMean(Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
getMeanAbsoluteError() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the mean absolute error (MAE), which is a common measure used to determine how close predictions are to the actual values.
getMeanActual() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the mean of the actual column.
getMeanActual() - Method in class com.bayesserver.data.R2CrossValidationTestResult: Gets the mean of the actual column values (as opposed to the predicted values).
getMeanSquaredError() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the mean squared error (MSE), which is a common measure used to determine how close predictions are to the actual values.
getMessage() - Method in interface com.bayesserver.data.discovery.DiscretizeProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.data.discovery.VariableGeneratorProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.PCStructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in interface com.bayesserver.learning.structure.StructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.learning.structure.TANStructuralLearningProgressInfo: Gets a progress message.
getMessage() - Method in class com.bayesserver.optimization.OptimizationWarning: Gets the warning message.
getMethod() - Method in class com.bayesserver.causal.BackdoorCriterionOptions
getMethod() - Method in class com.bayesserver.data.DataPartitioning: Gets the partitioning method.
getMethod() - Method in class com.bayesserver.learning.parameters.InitializationOptions: Determines the algorithm used for initialization.
getMethod() - Method in class com.bayesserver.learning.structure.LinkConstraint: Gets the method used to constrain nodes LinkConstraint.getA() and LinkConstraint.getB().
getMinDepth() - Method in class com.bayesserver.TopologicalSortNodeInfo: Gets the minimum number of links from a root node to this node.
getMinimum() - Method in class com.bayesserver.Interval: Gets the minimum interval value.
getMinimumEndPoint() - Method in class com.bayesserver.Interval: Gets the end point type for the minimum value of the interval.
getMissing() - Method in class com.bayesserver.data.discovery.VariableInfoCounts: Gets the count of missing/null values.
getMissingDataExclusions() - Method in class com.bayesserver.data.sampling.DataSamplingOptions: Variables can be added, to indicate that they should not generate missing values.
getMissingDataProbability() - Method in class com.bayesserver.data.sampling.DataSamplingOptions: When positive, sets a certain percentage of values to missing (except when DataSamplingOptions.getMissingDataProbabilityMin() has a value).
getMissingDataProbabilityMin() - Method in class com.bayesserver.data.sampling.DataSamplingOptions: When set, the missing data probability for each case varies randomly between DataSamplingOptions.getMissingDataProbabilityMin() and DataSamplingOptions.getMissingDataProbability().
getMissingProbability() - Method in class com.bayesserver.data.discovery.VariableInfo: Gets weighted and unweighted values between 0 and 1 indicating the percentage of data that is missing for this variable.
getMonitoredDistribution(Node) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo: Gets a copy of the current distribution assigned to the [node].
getMonitoredDistribution(Node, NodeDistributionKey) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo: Gets a copy of the current distribution assigned to the [node] at the requested order.
getMonitoredDistribution(Node, Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo: Gets a copy of the current distribution assigned to the [node] at the requested order.
getMonitorLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Calculates the log likelihood at each iteration.
getMutationProbability() - Method in class com.bayesserver.optimization.GeneticOptionsBase: The probability of genes being mutated.
getMutualInformation() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets the mutual information between X and Y, denoted I(X;Y).
getMutualInformation() - Method in class com.bayesserver.learning.structure.FeatureSelectionOptions: Gets a value which when true calculates the mutual information between each target and test.
getMutualInformation() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest: Gets the mutual information between the target and the test in NATS.
getName() - Method in class com.bayesserver.CustomProperty: Gets the name, which must be unique per CustomPropertyCollection.
getName() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets the name for the new variable.
getName() - Method in class com.bayesserver.learning.parameters.DistributerContext: Gets the name of the process/iteration being distributed.
getName() - Method in class com.bayesserver.Network: An optional name for the Bayesian network.
getName() - Method in class com.bayesserver.Node: The name of the node.
getName() - Method in class com.bayesserver.NodeGroup: Gets the name, which must be unique per NetworkNodeGroupCollection.
getName() - Method in class com.bayesserver.State: Gets the name of the state.
getName() - Method in class com.bayesserver.Variable: Gets the name of the variable.
getNestedTableCount() - Method in class com.bayesserver.data.DefaultDataReader: Gets the number of nested tables.
getNetwork() - Method in class com.bayesserver.causal.BackdoorCriterion: The Bayesian network on which the identification is based.
getNetwork() - Method in class com.bayesserver.causal.CausalInferenceBase: The target Bayesian network.
getNetwork() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: The Bayesian network on which the identification is based.
getNetwork() - Method in class com.bayesserver.causal.FrontDoorCriterion: The Bayesian network on which the identification is based.
getNetwork() - Method in interface com.bayesserver.causal.Identification: The Bayesian network on which the identification is based.
getNetwork() - Method in interface com.bayesserver.data.CrossValidationNetwork: Gets the network learnt from the cross validation partitioning.
getNetwork() - Method in class com.bayesserver.data.DefaultCrossValidationNetwork: Gets the network learnt from a cross validation partitioning.
getNetwork() - Method in class com.bayesserver.data.sampling.DataSampler: Gets the Bayesian network or Dynamic Bayesian network that was used in the constructor.
getNetwork() - Method in class com.bayesserver.inference.DefaultEvidence: Gets the Bayesian network that is the the target of the evidence.
getNetwork() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection: Gets the Network that is the target for a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getNetwork() - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection: Gets the Network that is the target for a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getNetwork() - Method in interface com.bayesserver.inference.Evidence: Gets the Bayesian network that is the the target of the evidence.
getNetwork() - Method in interface com.bayesserver.inference.Inference: The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.LikelihoodSamplingInference: The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.LoopyBeliefInference: The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.RelevanceTreeInference: The target Bayesian network.
getNetwork() - Method in class com.bayesserver.inference.VariableEliminationInference: The target Bayesian network.
getNetwork() - Method in class com.bayesserver.learning.parameters.DistributedMapperContext: Gets the Network that is being learnt by the distributed process.
getNetwork() - Method in class com.bayesserver.learning.parameters.ParameterLearning: Returns the relevant network.
getNetwork() - Method in class com.bayesserver.Link: The Network the link belongs to.
getNetwork() - Method in class com.bayesserver.NetworkLinkCollection: Gets the Network the collection belongs to.
getNetwork() - Method in class com.bayesserver.NetworkNodeCollection: The Network the collection belongs to.
getNetwork() - Method in class com.bayesserver.NetworkNodeGroupCollection: Gets the network instance that these groups belong to.
getNetwork() - Method in class com.bayesserver.NetworkVariableCollection: The Network the collection belongs to.
getNetwork() - Method in class com.bayesserver.Node: The Network the node belongs to.
getNode() - Method in class com.bayesserver.analysis.DSeparationTestResult: The test node.
getNode() - Method in class com.bayesserver.analysis.ParameterReference: Gets the node whose distribution parameter is being referenced.
getNode() - Method in class com.bayesserver.causal.AdjustmentSetNode: Gets the node.
getNode() - Method in class com.bayesserver.causal.CausalNode: Gets the Bayesian network node.
getNode() - Method in class com.bayesserver.causal.DisjunctiveCauseSetNode: Gets the node.
getNode() - Method in class com.bayesserver.causal.FrontDoorSetNode: Gets the node.
getNode() - Method in interface com.bayesserver.causal.NodeSetItem: Gets the node.
getNode() - Method in class com.bayesserver.inference.AssignedDefinition: The node that is assigned (multiplied into) to the clique in a junction tree.
getNode() - Method in class com.bayesserver.inference.EliminationDefinition: The node that has been elimiated.
getNode() - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Gets the Node this distribution specification refers to.
getNode() - Method in class com.bayesserver.NodeDistributionExpressions: Gets the node that this instance belongs to.
getNode() - Method in class com.bayesserver.NodeDistributionOptions: The node this instance belongs to.
getNode() - Method in class com.bayesserver.NodeDistributions: Gets the node that this instance belongs to.
getNode() - Method in class com.bayesserver.NodeGroupCollection: The Node the collection belongs to.
getNode() - Method in class com.bayesserver.NodeLinkCollection: Gets the Node to which the collection belongs to.
getNode() - Method in class com.bayesserver.NodeVariableCollection: The Node the collection belongs to.
getNode() - Method in class com.bayesserver.TopologicalSortNodeInfo: Gets the node in the network.
getNode() - Method in class com.bayesserver.UnrollOutput.NodeTime: Gets the node.
getNode() - Method in class com.bayesserver.Variable: Gets the Node this instance belongs to, if any.
getNodeGroups() - Method in class com.bayesserver.Network: Gets groups which nodes can belong to.
getNodes() - Method in class com.bayesserver.causal.AdjustmentSet: Gets the adjustment set nodes.
getNodes() - Method in class com.bayesserver.causal.DisjunctiveCauseSet: Gets the nodes in the set.
getNodes() - Method in class com.bayesserver.causal.FrontDoorSet: Gets the front-door nodes used by the front-door adjustment, and can be identified using the front-door criterion.
getNodes() - Method in interface com.bayesserver.causal.NodeSet: Gets the list of nodes in the set.
getNodes() - Method in class com.bayesserver.Network: The collection of nodes in the Bayesian network.
getNodeWidthOverride() - Method in class com.bayesserver.DecomposeOptions: Gets a value that can be used to override the width of nodes, used when laying out new nodes.
getNodeWidthOverride() - Method in class com.bayesserver.UnrollOptions: Gets a value that can be used to override the width of nodes, used when laying out nodes.
getNoisyOrder() - Method in class com.bayesserver.Link: Gets a value which determines the nature of the effect between the parent node (from) and a noisy child node (to).
getNoisyType() - Method in class com.bayesserver.NodeDistributionOptions: Gets a value which identifies this node as a noisy node or not.
getNormalization() - Method in class com.bayesserver.TableExpression: Gets of sets the normalization method, if any, to use once the Table values have been generated, but before assignment to a node.
getNotMissing() - Method in class com.bayesserver.data.discovery.VariableInfoCounts: Gets the counts of values that are not missing/null values.
getObject(int) - Method in class com.bayesserver.data.DataReaderFiltered
getObject(int) - Method in interface com.bayesserver.data.DataRecord: Gets an Object representation for the value at the specified column.
getObject(int) - Method in class com.bayesserver.data.DataTableReader
getObject(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets an Object representation for the value at the specified column.
getObjectiveValue() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput: The objective value.
getObjectiveValue() - Method in class com.bayesserver.optimization.GeneticOptimizerProgressInfo: Gets the optimized objective (target) value.
getObjectiveValue() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput: The objective value.
getObjectiveValue() - Method in interface com.bayesserver.optimization.OptimizerOutput: The objective value.
getObjectiveValue() - Method in interface com.bayesserver.optimization.OptimizerProgressInfo: Gets the optimized objective (target) value.
getOneMinusPValue() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest: Gets a value which equals one minus the p-value returned from the statical independence test.
getOnExecuteReader() - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand: Gets a function that is called when a new reader is created.
getOptions() - Method in class com.bayesserver.data.discovery.DiscretizationColumn: Gets the discretization options for this column of data.
getOrder() - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Gets the order of the distribution, for temporal nodes.
getOrder() - Method in class com.bayesserver.NodeDistributionKey: Gets the temporal order of the related node distribution.
getOriginalNetwork() - Method in class com.bayesserver.DecomposeOutput: Gets the original network, containing nodes with multiple variables.
getOriginalVariable(Variable) - Method in class com.bayesserver.DecomposeOutput: Maps a variable in the decomposed network to the equivalent variable in the original network.
getOutcome() - Method in class com.bayesserver.causal.EffectsAnalysisOutput: Gets the outome (target) variable on which effects are being measured.
getOutcomeDistribution() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets P(Outcome|Do(Treatment=TreatmentState)) for discrete treatments and P(Outcome|Do(Treatment=TreatmentValue)) for continuous treatments.
getOutcomeFunctionOutput() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets the function output when the outcome is a function.
getOutcomeMean() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets the mean of the outcome for this treatment (cause).
getOutcomeVariance() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets the variance of the outcome for this treatment (cause).
getOuter() - Method in class com.bayesserver.CLGaussian
getOuter() - Method in interface com.bayesserver.Distribution: Returns the parent distribution, if this instance is aggregated by another distribution.
getOuter() - Method in class com.bayesserver.Table
getOwner() - Method in class com.bayesserver.CLGaussian: Gets the current owner, if assigned to a node.
getOwner() - Method in class com.bayesserver.CustomPropertyCollection: Gets the instance that these custom properties belong to.
getOwner() - Method in interface com.bayesserver.Distribution: Gets the current owner, if assigned to a node.
getOwner() - Method in interface com.bayesserver.DistributionExpression: Gets the current owner, if assigned to a node.
getOwner() - Method in class com.bayesserver.FunctionVariableExpression: Gets the current owner, if assigned to a variable.
getOwner() - Method in interface com.bayesserver.QueryExpression: Gets the current owner, if assigned to a variable.
getOwner() - Method in class com.bayesserver.Table: Gets the current owner, if assigned to a node.
getOwner() - Method in class com.bayesserver.TableExpression: Gets the current owner, if assigned to a node.
getPair() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets the pair (X, Y) that these association results are calculated for.
getPairOutputs() - Method in class com.bayesserver.analysis.AssociationOutput: Gets the output for each pair.
getParameterCount(Network) - Static method in class com.bayesserver.ParameterCounter: Gets the number of parameters in a Bayesian network.
getParameterCount(Network, ParameterCountOptions) - Static method in class com.bayesserver.ParameterCounter: Gets the number of parameters in a Bayesian network.
getParameterCount(Node, int) - Static method in class com.bayesserver.ParameterCounter: Gets the parameter count for an individual node distribution.
getParameterCount(Node, NodeDistributionKey) - Static method in class com.bayesserver.ParameterCounter: Gets the parameter count for an individual node distribution.
getParameterValue() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets the original value of the parameter being analyzed.
getParameterValue1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets the original value of the first parameter being analyzed (t1).
getParameterValue2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets the original value of the second parameter being analyzed (t2).
getParent() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the parent collection.
getParent() - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection: Gets the parent variable output.
getParent() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput: Gets the parent collection.
getParent() - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection: Gets the main output.
getParent() - Method in class com.bayesserver.CustomProperty: Gets the parent collection, if set, otherwise null.
getParent() - Method in class com.bayesserver.inference.SepsetDefinition: The parent clique in the junction tree.
getParent() - Method in class com.bayesserver.NodeGroup: Gets the parent collection, if set, otherwise null.
getParents() - Method in class com.bayesserver.inference.CliqueDefinition: The parent nodes in the junction tree (if any).
getPartition() - Method in class com.bayesserver.data.CrossValidationOutput: Gets the zero based index of the partition.
getPartitionCount() - Method in class com.bayesserver.data.DataPartitioning: Gets the total number of partitions.
getPartitionCount() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets the number of partitions used by scoring functions that use cross validation.
getPartitionNumber() - Method in class com.bayesserver.data.DataPartitioning: Gets the zero based partition number.
getPartitions() - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets the number of cross validation partitions to use.
getPartitions() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions: Gets the number of cross validation partitions to use.
getPartitions() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the number of cross validation partitions to use when scoring each cluster count.
getPartitions() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the number of cross validation partitions to use when scoring each cluster count.
getPoints() - Method in class com.bayesserver.analysis.LiftChart: Gets the xy points that make up the lift chart.
getPopulationSize() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets the number of chromosomes in each generation.
getPredictedProbability() - Method in class com.bayesserver.analysis.LiftChart: Gets the name of the data column which contains the predicted probability generated by an inference query.
getPriors() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Contains parameters used to avoid boundary conditions during learning.
getProbability() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the probability of this state with no evidence set on the target state.
getProbability() - Method in class com.bayesserver.analysis.ConfusionMatrixCell: Gets the overall probability for this cell.
getProbabilityGivenActual() - Method in class com.bayesserver.analysis.ConfusionMatrixCell: Gets the probability for this cell, conditional on the actual counts.
getProbabilityGivenPredicted() - Method in class com.bayesserver.analysis.ConfusionMatrixCell: Gets the probability for this cell, conditional on the predicted counts.
getProbabilityGivenTarget() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the probability of this state given the target state.
getProbabilityGivenTarget() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput: Gets the distribution of this variable given the target.
getProbabilityHypothesisGivenEvidence() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets P(h|e).
getProbabilityHypothesisGivenEvidence() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay: Gets P(h|e).
getProbabilityTargetGivenThis() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the probability of the target state given this state.
getProficiencyXGivenY() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets the proficiency (uncertainty coefficient) U(X|Y) = I(X;Y) / H(X).
getProficiencyYGivenX() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets the proficiency (uncertainty coefficient) U(Y|X) = I(X;Y) / H(Y).
getProgress() - Method in class com.bayesserver.data.discovery.Clustering: Gets an instance that receive progress notifications.
getProgress() - Method in interface com.bayesserver.data.discovery.Discretize: Gets an instance that receive progress notifications.
getProgress() - Method in class com.bayesserver.data.discovery.EqualFrequencies: Gets an instance that receive progress notifications.
getProgress() - Method in class com.bayesserver.data.discovery.EqualIntervals: Gets an instance that receive progress notifications.
getProgress() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets of sets the instance implementing VariableGeneratorProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets of sets the instance implementing ParameterLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
getProgress() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets of sets the instance implementing OptimizerProgress, used for progress notifications.
getProgress() - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets of sets the instance implementing OptimizerProgress, used for progress notifications.
getPropagation() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets the propagation method to be used during inference.
getPropagation() - Method in interface com.bayesserver.inference.QueryOptions: Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Gets the propagation method to be used during inference.
getPropagation() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions: Gets the propagation method to use during marginalization.
getQueryCount() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput: Gets the number of call made to the inference engine(s) during optimization.
getQueryCount() - Method in class com.bayesserver.optimization.GeneticOptimizerProgressInfo: Gets the number of calls made to inference engines during optimization.
getQueryCount() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput: Gets the number of call made to the inference engine(s) during optimization.
getQueryCount() - Method in interface com.bayesserver.optimization.OptimizerOutput: The number of queries to inference engines performed during optimization.
getQueryCount() - Method in interface com.bayesserver.optimization.OptimizerProgressInfo: Gets the number of calls made to inference engines during optimization.
getQueryDistance() - Method in class com.bayesserver.inference.QueryDistribution: Gets a value indicating whether the distance should be calculated between the query calculated with base evidence (or no evidence), and the same query calculated with evidence.
getQueryDistributions() - Method in class com.bayesserver.causal.CausalInferenceBase: Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.causal.EffectsAnalysisOptions: Determines which additional queries, if any, should be calculated by the inference engine.
getQueryDistributions() - Method in interface com.bayesserver.inference.Inference: Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.LoopyBeliefInference: Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.RelevanceTreeInference: Gets the collection of distributions to calculate.
getQueryDistributions() - Method in class com.bayesserver.inference.VariableEliminationInference: The collection of distributions required from a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
getQueryDistributions() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Determines which additional queries, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
getQueryDistributions() - Method in interface com.bayesserver.optimization.OptimizerOptions: Determines which additional queries, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
getQueryEvidenceMode() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in interface com.bayesserver.inference.QueryOptions: Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.TreeQueryOptions: Determines whether evidence is retracted for each query.
getQueryEvidenceMode() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Determines whether evidence is retracted for each query.
getQueryFunctions() - Method in class com.bayesserver.causal.CausalInferenceBase: Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
getQueryFunctions() - Method in class com.bayesserver.causal.EffectsAnalysisOptions: Determines which additional functions, if any, should be calculated by the inference engine.
getQueryFunctions() - Method in interface com.bayesserver.inference.Inference: Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
getQueryFunctions() - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
getQueryFunctions() - Method in class com.bayesserver.inference.LoopyBeliefInference: Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
getQueryFunctions() - Method in class com.bayesserver.inference.RelevanceTreeInference: Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
getQueryFunctions() - Method in class com.bayesserver.inference.VariableEliminationInference: Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
getQueryFunctions() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Determines which additional functions, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
getQueryFunctions() - Method in interface com.bayesserver.optimization.OptimizerOptions: Determines which additional functions, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
getQueryLifecycle() - Method in class com.bayesserver.causal.CausalInferenceBase: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
getQueryLifecycle() - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Gets a query lifecycle instance.
getQueryLifecycle() - Method in interface com.bayesserver.inference.Inference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
getQueryLifecycle() - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
getQueryLifecycle() - Method in class com.bayesserver.inference.LoopyBeliefInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
getQueryLifecycle() - Method in class com.bayesserver.inference.RelevanceTreeInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
getQueryLifecycle() - Method in class com.bayesserver.inference.VariableEliminationInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
getQueryLogLikelihood() - Method in class com.bayesserver.inference.QueryDistribution: Determines whether or not to calculate the QueryDistribution.getLogLikelihood() specific to the evidence used to calculate this query.
getQueryLogLikelihood() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
getQueryLogLikelihood() - Method in interface com.bayesserver.optimization.OptimizerOptions: Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
getQueryOptions() - Method in interface com.bayesserver.inference.QueryLifecycleBegin: The query options instance being used in the query.
getQueryOptions() - Method in class com.bayesserver.inference.QueryLifecycleBeginBase: The query options instance being used in the query.
getQueryOptions() - Method in interface com.bayesserver.inference.QueryLifecycleEnd: The query options instance being used in the query.
getQueryOptions() - Method in class com.bayesserver.inference.QueryLifecycleEndBase: The query options instance being used in the query.
getQueryOutput() - Method in interface com.bayesserver.inference.QueryLifecycleEnd: The query output.
getQueryOutput() - Method in class com.bayesserver.inference.QueryLifecycleEndBase: The query output.
getQueryTimeout() - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Gets the timeout to be used when statements are executed.
getReadInfo() - Method in class com.bayesserver.data.DefaultEvidenceReader: Provides information about the last read of non temporal data.
getRecord() - Method in class com.bayesserver.data.NestedReadInfo: The current nested table record.
getRecord() - Method in class com.bayesserver.data.ReadInfo: The current record.
getRecord() - Method in class com.bayesserver.data.TemporalReadInfo: The current temporal record.
getRelatedNode() - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Gets the related node (if any) of the distribution.
getRelatedNode() - Method in class com.bayesserver.NodeDistributionKey: Gets the parent of the noisy node this distribution refers to, or the noisy node itself to identify the leak distribution.
getRemoveAbductionEvidence() - Method in class com.bayesserver.causal.AbductionOptions: Gets a value which when true removes the abduction evidence, after updating the characteristic variables.
getReturnType() - Method in interface com.bayesserver.Expression: Gets the return type of the expression.
getReturnType() - Method in class com.bayesserver.FunctionVariableExpression: Gets the return type of the expression.
getReturnType() - Method in class com.bayesserver.TableExpression: Gets the return type of the expression.
getRMSE() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the root mean squared error (RMSE).
getRoot() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets the root of the Chow-Liu tree.
getRoot() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets the root of the TAN tree.
getRow() - Method in class com.bayesserver.TableIterator: Gets the current position of the iterator.
getRow(int[]) - Method in class com.bayesserver.TableAccessor: Gets the TableAccessor row for the given states.
getRows() - Method in class com.bayesserver.data.DataTable: Gets the rows of data in the table.
getRSquared() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the R squared value (Coefficient of determination).
getRunsPerConfiguration() - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets of sets the number of times training is re-run for each network structure tested.
getRunsPerConfiguration() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the number of times training is re-run for each network structure tested.
getRunsPerConfiguration() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the number of times training is re-run for each network structure tested.
getSampleCount() - Method in class com.bayesserver.analysis.AutoInsightSamplingOptions: The number of samples used to approximate sufficient statistics, when exact inference is not possible.
getSampleCount() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets a value indicating how many samples cases to generate in order to approximate the current query.
getSampleCount() - Method in interface com.bayesserver.inference.QuerySamplingOptions: Gets a value indicating how many samples cases to generate in order to approximate the current query.
getSampling() - Method in class com.bayesserver.analysis.AutoInsightOptions: Options affecting sampling, when approximate inference is required.
getSamplingProbability() - Method in class com.bayesserver.learning.parameters.InitializationOptions: A value between 0 and 1 (inclusive) indicating what probability of cases to use for initialization.
getSaveHyperparameters() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets a value indicating whether hyperparameters (e.g.
getScore() - Method in class com.bayesserver.analysis.ClusterScore: The score achieved for this number of clusters.
getScore() - Method in class com.bayesserver.analysis.LiftChart: Gets the overall score, which is a positive or negative probability between 0 and 1 indicating the classification performance of the network.
getScore() - Method in interface com.bayesserver.data.CrossValidationScore: Gets the combined score over each cross validation partitioning.
getScore() - Method in class com.bayesserver.data.DefaultCrossValidationScore: Gets the combined score over each cross validation partitioning.
getScoreMethod() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets the scoring method used to evaluate search moves.
getScores() - Method in class com.bayesserver.analysis.ClusterCountOutput: A list of scores, one for each cluster count in the same order passed to ClusterCount.
getSeed() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets an optional seed for the random number generator.
getSeed() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Gets the seed used by the random number generator.
getSeed() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the seed used to generate random numbers for initialization.
getSeed() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the seed used to generate random numbers for initialization.
getSeed() - Method in class com.bayesserver.optimization.GeneticOptionsBase: The seed for the random number generator used by the Genetic Algorithm.
getSensitivityValue() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay: Gets the sensitivity value which is the derivative of the sensitivity function evaluated at t.
getSepsets() - Method in class com.bayesserver.inference.JunctionTreesDefinition: The Sepsets in the junction tree(s).
getSequenceLength() - Method in class com.bayesserver.data.sampling.DataSamplingOptions: The sequence length generated for each sample from networks with temporal nodes.
getSets() - Method in class com.bayesserver.causal.BackdoorCriterionOutput: Gets a list of identified 'adjustment sets'.
getSets() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOutput: Gets an adjustment set which includes all causes of treatments (X) or causes of outcomes (Y) or causes of both.
getSets() - Method in class com.bayesserver.causal.FrontDoorCriterionOutput: Gets a list of front-door node sets.
getShift() - Method in class com.bayesserver.data.timeseries.WindowOptions: Gets the number of records between successive windows.
getSignificanceLevel() - Method in class com.bayesserver.learning.structure.IndependenceOptions: Gets the significance level used to accept or reject (conditional) independence tests.
getSimpleVariance() - Method in class com.bayesserver.learning.parameters.Priors: Used to make a fixed adjustment to all covariance matrices during learning, by increasing each diagonal (variance) entry.
getSimplifyTolerance() - Method in class com.bayesserver.optimization.GeneticSimplificationOptions: This is a non negative number which determines whether a simplified solution is close enough to the best found.
getSize() - Method in class com.bayesserver.inference.CliqueDefinition: The size (number of parameters) of the distribution, if it were to be instantiated.
getSize() - Method in interface com.bayesserver.inference.JunctionTreeNodeDefinition: The size of the distribution, were it to be instantiated.
getSize() - Method in class com.bayesserver.inference.SepsetDefinition: The size (number of parameters) of the distribution, if it were to be instantiated.
getSliceCount() - Method in class com.bayesserver.UnrollOutput: Gets the slice count of the unrolled network.
getSliceGap() - Method in class com.bayesserver.UnrollOptions: Gets the gap between time slices.
getSortedContinuousHead() - Method in class com.bayesserver.CLGaussian: Gets the collection of continuous head variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedContinuousTail() - Method in class com.bayesserver.CLGaussian: Gets the collection of continuous tail variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedIndex(State...) - Method in class com.bayesserver.Table: Gets the index of the table element that corresponds to a particular combination of states.
getSortedIndex(StateContext...) - Method in class com.bayesserver.Table: Gets the index of the table element that corresponds to a particular combination of states and their times.
getSortedUniqueValues() - Method in class com.bayesserver.analysis.ConfusionMatrix: Gets a sorted list of unique values which is the union of the different actual and predicted values found.
getSortedVariables() - Method in class com.bayesserver.CLGaussian: Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedVariables() - Method in interface com.bayesserver.Distribution: Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortedVariables() - Method in class com.bayesserver.Table: Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
getSortOrder() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets the sort order for states of a new discrete variable.
getStagnationCount() - Method in class com.bayesserver.optimization.GeneticTerminationOptions: Gets the number of generations with equal objective values that are evaluated before the optimizer terminates.
getState() - Method in class com.bayesserver.analysis.AutoInsightStateOutput: Gets the state this insight refers to.
getState() - Method in class com.bayesserver.optimization.DesignState: Gets the state these options refer to.
getState() - Method in class com.bayesserver.optimization.Objective: Gets the state being optimized.
getState() - Method in class com.bayesserver.StateContext: Gets the State.
getState(int) - Method in class com.bayesserver.TableIterator: Gets the state for an individual node indexed by the order of nodes in the TableIterator.
getState(int, int) - Method in class com.bayesserver.TableAccessor: Gets the state at the given position [i] for the node given by [node].
getState(Node) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node) - Method in interface com.bayesserver.inference.Evidence: Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Node, Integer) - Method in interface com.bayesserver.inference.Evidence: Gets the hard evidence state for node with a single variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable) - Method in interface com.bayesserver.inference.Evidence: Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getState(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Gets the hard evidence state for a particular variable, or returns null if the EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT.
getStateAllDiffThis() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the probability of the hypothesis state (if specified) with all evidence to analyze minus the state probability for this evidence configuration.
getStateAllLiftThis() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the probability of the hypothesis state (if specified) when all evidence to analyze is set relative to when this evidence configuration is set.
getStateNotFoundAction() - Method in class com.bayesserver.data.VariableReference: Determines the action to take if the name or value from the data cannot be matched to a particular state for this reference variable.
getStateOutputs() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput: Gets the insight for each state of this test variable.
getStateProbability() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the probability of the hypothesis state (if specified) for this output item evidence.
getStateProbabilityAll() - Method in class com.bayesserver.analysis.ImpactHypothesisOutput: Gets the probability of the hypothesis state (if any) with all evidence to analyze set.
getStateProbabilityNone() - Method in class com.bayesserver.analysis.ImpactHypothesisOutput: Gets the probability of the hypothesis state (if any) with no evidence to analyze set.
getStates() - Method in class com.bayesserver.analysis.ParameterReference: Gets the states which together locate a specific parameter in the node's distribution.
getStates() - Method in class com.bayesserver.State: Gets the StateCollection the state belongs to, if any.
getStates() - Method in class com.bayesserver.Variable: Returns the collection of states belonging to the variable.
getStates(int[]) - Method in class com.bayesserver.TableIterator: Gets the states of all nodes, based on the order of nodes in the TableIterator not the underlying Table.
getStates(int, int[]) - Method in class com.bayesserver.TableAccessor: Gets the states at the given position [i].
getStates(Node, double[]) - Method in class com.bayesserver.inference.DefaultEvidence: Fills out a buffer containing the soft evidence for a node with a single variable.
getStates(Node, double[]) - Method in interface com.bayesserver.inference.Evidence: Fills out a buffer containing the soft evidence for a node with a single variable.
getStates(Node, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
getStates(Node, double[], Integer) - Method in interface com.bayesserver.inference.Evidence: Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
getStates(Table) - Method in class com.bayesserver.inference.DefaultEvidence: Fills out a table containing the soft evidence for a particular variable.
getStates(Table) - Method in interface com.bayesserver.inference.Evidence: Fills out a table containing the soft evidence for a particular variable.
getStates(Variable, double[]) - Method in class com.bayesserver.inference.DefaultEvidence: Fills out a buffer containing the soft evidence for a particular variable.
getStates(Variable, double[]) - Method in interface com.bayesserver.inference.Evidence: Fills out a buffer containing the soft evidence for a particular variable.
getStates(Variable, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Fills out a buffer containing the soft evidence for a particular variable at a specified time.
getStates(Variable, double[], Integer) - Method in interface com.bayesserver.inference.Evidence: Fills out a buffer containing the soft evidence for a particular variable at a specified time.
getStateThisDiffNone() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the probability of the hypothesis state (if specified) for this evidence configuration minus the state probability with no evidence to analyze.
getStateThisLiftNone() - Method in class com.bayesserver.analysis.ImpactOutputItem: Gets the probability of the hypothesis state (if specified) for this evidence configuration relative to when no evidence to analyze is set.
getStateValueType() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets the StateValueType for the new variable.
getStateValueType() - Method in class com.bayesserver.Variable: Gets the type of value that states belonging to this variable can represent.
getStop() - Method in interface com.bayesserver.Stop: When true, indicates to the algorithm to complete early.
getStopping() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets the instance implementing Stop used for early stopping.
getStopping() - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets the instance implementing Stop used for early stopping.
getString(int) - Method in class com.bayesserver.data.DataReaderFiltered
getString(int) - Method in interface com.bayesserver.data.DataRecord: Gets a string value for the specified column.
getString(int) - Method in class com.bayesserver.data.DataTableReader
getString(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Gets a string value for the specified column.
getSubsetMethod() - Method in class com.bayesserver.analysis.ImpactOptions: Gets a value which determines whether evidence subsets are included, excluded or both.
getSubsetMethod() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Gets a value which determines whether evidence subsets are included, excluded or both.
getSuggestedBinCount() - Method in class com.bayesserver.analysis.HistogramDensityOptions: Gets the approximate number of bins to use to represent the approximate density function.
getSuggestedBinCount() - Method in class com.bayesserver.data.discovery.DiscretizationOptions: Gets the number of suggested bins to use during discretization.
getSumAbsoluteError() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the sum absolute error (SAE).
getSumSquaredError() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the sum of squared errors (SSE), which is a common measure used to determine how close predictions are to the actual values.
getSupport() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the support/weight for the values used to calculate the statistics.
getSymmetricMutualInformation() - Method in class com.bayesserver.analysis.AssociationPairOutput: Gets a normalized version of the mutual information called the 'symmetric uncertainty' between X and Y.
getSyncNodeVariableName() - Static method in class com.bayesserver.Network: When true synchronizes Variable names with their containing Node.
getTable() - Method in class com.bayesserver.CLGaussian: Gets the Table which specifies the distribution over any discrete variables.
getTable() - Method in class com.bayesserver.data.DataColumn
getTable() - Method in class com.bayesserver.data.DataRow
getTable() - Method in interface com.bayesserver.Distribution: Gets the Table which specifies the distribution over any discrete variables.
getTable() - Method in class com.bayesserver.Table
getTable() - Method in class com.bayesserver.TableAccessor: Gets the underlying Table.
getTable() - Method in class com.bayesserver.TableIterator: Gets the underlying Table.
getTableIndex(int) - Method in class com.bayesserver.TableAccessor: Gets the equivalent index in the underlying table that corresponds to the index in the accessor.
getTableRow() - Method in class com.bayesserver.TableIterator: Gets the position of the iterator in the underlying Table.
getTarget() - Method in class com.bayesserver.analysis.AutoInsightOutput: Gets the target state used to calculate the insight.
getTarget() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest: Gets the variable that was the target of the feature selection test.
getTarget() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets the target of the TAN tree.
getTargetProbability() - Method in class com.bayesserver.analysis.AutoInsightOutput: Gets the probability of the target state, given any optional background evidence.
getTargetValue() - Method in class com.bayesserver.analysis.LiftChart: Gets the target value which we are interested in.
getTemporalOrder() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest: Gets the temporal order (if any) used to test the variables.
getTemporalOrder() - Method in class com.bayesserver.learning.structure.LinkConstraint: Gets the temporal order of the constraint.
getTemporalOrder() - Method in class com.bayesserver.Link: Gets the temporal order of the link.
getTemporalOrder() - Method in class com.bayesserver.NodeDistributionExpressions.DistributionExpressionOrder: Gets the temporal order of the distribution expression.
getTemporalOrder() - Method in class com.bayesserver.NodeDistributions.DistributionOrder: Gets the temporal order of the distribution.
getTemporalReadInfo() - Method in class com.bayesserver.data.DefaultEvidenceReader: Provides information about the last read of temporal data.
getTemporalType() - Method in class com.bayesserver.Node: The TemporalType of the node.
getTerminalTime() - Method in class com.bayesserver.analysis.DSeparationOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.analysis.ValueOfInformationOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in interface com.bayesserver.inference.QueryOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTerminalTime() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
getTermination() - Method in class com.bayesserver.optimization.GeneticOptionsBase: Termination options.
getTestIndependence() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets a value which when true uses independence tests to reduce the search space.
getTestOutputs() - Method in class com.bayesserver.analysis.ValueOfInformationOutput: Gets the result of tests carried out on the test variables.
getTestPartitioning() - Method in class com.bayesserver.data.CrossValidationOutput: Gets the test DataPartitioning associated with this partition.
getTestResults() - Method in class com.bayesserver.analysis.DSeparationOutput: The collection of test results.
getTests() - Method in class com.bayesserver.learning.structure.FeatureSelectionOutput: Gets the tests carried out for each variable against the target.
getTestSingleCluster() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets a value which determines whether a test is performed for a single cluster (i.e.
getText() - Method in interface com.bayesserver.Expression: Gets the expression text.
getText() - Method in class com.bayesserver.FunctionVariableExpression: Gets the expression text.
getText() - Method in class com.bayesserver.TableExpression: Gets the expression text, which is run for each cell in the table.
getTime() - Method in class com.bayesserver.analysis.DSeparationTestResult: The zero based time at which the test was performed, or null if the node is not temporal.
getTime() - Method in class com.bayesserver.causal.AdjustmentSetNode: Gets the node time, for temporal nodes.
getTime() - Method in class com.bayesserver.causal.CausalNode: Gets the optional time, required for temporal nodes.
getTime() - Method in class com.bayesserver.causal.DisjunctiveCauseSetNode: Gets the node time, for temporal nodes.
getTime() - Method in class com.bayesserver.causal.FrontDoorSetNode: Gets the node time, for temporal nodes.
getTime() - Method in interface com.bayesserver.causal.NodeSetItem: Gets the node time, for temporal nodes.
getTime() - Method in class com.bayesserver.inference.AssignedDefinition: The associated time (if a Dynamic Bayesian network).
getTime() - Method in class com.bayesserver.inference.EliminationDefinition: The associated time (if a Dynamic Bayesian network).
getTime() - Method in class com.bayesserver.StateContext: Gets the zero based time associated with the state if the state belongs to a temporal node, or null otherwise.
getTime() - Method in class com.bayesserver.UnrollOutput.NodeTime: Gets the time of the node, or null if a time is not appropriate for the temporal type of the node.
getTime() - Method in class com.bayesserver.UnrollOutput.VariableTime: Gets the time of the variable, or null if a time is not appropriate for the temporal type of the variable.
getTime() - Method in class com.bayesserver.VariableContext: Gets the time associated with the variable if it belongs to a temporal node.
getTimeColumn() - Method in class com.bayesserver.data.TemporalReaderOptions: The name of the time column in the temporal data, if temporal data is present.
getTimeIndex() - Method in class com.bayesserver.data.TemporalReadInfo: Gets the zero based time index (e.g.
getTimes() - Method in class com.bayesserver.data.timeseries.WindowOptions: Gets the times to include in the window.
getTimeSeriesMode() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the mode in which time series distributions are learned.
getTimeValue() - Method in class com.bayesserver.data.TemporalReadInfo: Gets the current value in the time column.
getTimeValueType() - Method in class com.bayesserver.data.TemporalReaderOptions: The type of values contained in the time column.
getTo() - Method in class com.bayesserver.Link: The child node of the directed link.
getTolerance() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Gets the tolerance used to determine whether or not the approximate inference process has converged.
getTolerance() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets the tolerance used to determine whether or not parameter learning has converged.
getTolerance() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets the tolerance used to determine whether or not a search move is a significant improvement.
getToleranceOrDefault() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: If Tolerance is null, this returns the default tolerance for the given convergence method, otherwise Tolerance is returned.
getToleranceOrDefault() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: If Tolerance is null, this returns the default tolerance for the given scoring method, otherwise Tolerance is returned.
getTotalCount() - Method in class com.bayesserver.analysis.ConfusionMatrix: Gets a count of the number of predictions, whether they were correct or not.
getTrainingPartioning() - Method in class com.bayesserver.data.CrossValidationOutput: Gets the training DataPartitioning associated with this partition.
getTreatment() - Method in class com.bayesserver.causal.EffectsAnalysisOutput: Gets the treatment variable which is being varied.
getTreatmentState() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets the treatment state used to measure the causal effect on the treatment.
getTreatmentValue() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets the treatment value used to measure the causal effect on the treatment.
getTreatmentValues() - Method in class com.bayesserver.causal.EffectsAnalysisOptions: A list of treatment values to evaluate the causal effect on the outcome for.
getTreatmentVariable() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem: Gets the treatment variable used to measure the causal effect on the treatment.
getTreeWidth() - Method in class com.bayesserver.inference.TreeQueryOptions: Gets a value indicating whether or not to calculate the tree width.
getTreeWidth() - Method in class com.bayesserver.inference.TreeQueryOutput: Gets the tree width, if requested.
getUnrolled() - Method in class com.bayesserver.UnrollOutput: Gets the unrolled Dynamic Bayesian network.
getUnrolledNode(Node, Integer) - Method in class com.bayesserver.UnrollOutput: Maps between a node in the original Dynamic Bayesian network, and the corresponding node in the unrolled network.
getUnrolledVariable(Variable, Integer) - Method in class com.bayesserver.UnrollOutput: Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
getUnweighted() - Method in class com.bayesserver.data.discovery.VariableInfoCount: The number of records.
getUnweighted() - Method in class com.bayesserver.data.discovery.VariableInfoValue: Gets the unweighted value.
getUnweightedCaseCount() - Method in class com.bayesserver.data.DataProgressEventArgs: Gets the number of cases read so far.
getUnweightedCaseCount() - Method in class com.bayesserver.data.DefaultEvidenceReader: Gets the number of cases (unweighted) read so far.
getUnweightedCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the unweighted case count in the learning data.
getUnweightedTemporalCount() - Method in class com.bayesserver.data.DataProgressEventArgs: Gets the number of temporal rows read so far for all cases.
getUpperBound() - Method in class com.bayesserver.optimization.DesignState: The maximum value allowed for this variable/state during the optimization process.
getValue() - Method in class com.bayesserver.CustomProperty: The custom property value.
getValue() - Method in interface com.bayesserver.data.CrossValidationTestResult: Gets the test result value for this test partitioning.
getValue() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult: Gets the test result value for this test partitioning.
getValue() - Method in class com.bayesserver.data.discovery.WeightedValue: Gets the value, which can be null.
getValue() - Method in class com.bayesserver.data.R2CrossValidationTestResult: Gets the test result value for this test partitioning.
getValue() - Method in class com.bayesserver.inference.QueryFunctionOutput: Holds the result of a function evaluation at query time.
getValue() - Method in class com.bayesserver.optimization.Objective: Gets the objective target value.
getValue() - Method in class com.bayesserver.State: Gets an optional value for a state, such as an interval for discretized variables.
getValue() - Method in class com.bayesserver.Table.MaxValue
getValue() - Method in class com.bayesserver.TableIterator: Gets the underlying Table value at the current position of the iterator.
getValueType() - Method in class com.bayesserver.data.discovery.VariableDefinition: Gets the VariableValueType for the new variable.
getValueType() - Method in class com.bayesserver.Variable: Gets the variable's value type, e.g.
getVariable() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput: Gets the test variable.
getVariable() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput: Gets the variable that was tested.
getVariable() - Method in class com.bayesserver.data.discovery.VariableInfo: Gets the generated Variable.
getVariable() - Method in class com.bayesserver.data.VariableReference: Gets the variable.
getVariable() - Method in class com.bayesserver.inference.QueryFunctionOutput: The function variable to evaluate.
getVariable() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest: Gets the variable which was tested to see if it is likely to be a feature of the FeatureSelectionTest.getTarget() variable.
getVariable() - Method in class com.bayesserver.optimization.DesignVariable: Gets the variable these options refer to.
getVariable() - Method in class com.bayesserver.optimization.Objective: Gets the variable being optimized.
getVariable() - Method in class com.bayesserver.State: Gets the Variable the state belongs to, if any.
getVariable() - Method in class com.bayesserver.StateCollection: Gets the Variable this collection belongs to.
getVariable() - Method in class com.bayesserver.UnrollOutput.VariableTime: Gets the variable.
getVariable() - Method in class com.bayesserver.VariableContext: Gets the variable.
getVariableContexts() - Method in class com.bayesserver.inference.CliqueDefinition: The variables in the clique (optionally with times for DBNs).
getVariableContexts() - Method in interface com.bayesserver.inference.JunctionTreeNodeDefinition: The variables in the clique or sepset (optionally with a time).
getVariableContexts() - Method in class com.bayesserver.inference.SepsetDefinition: The variables in the sepset (optionally with times for DBNs).
getVariableOutputs() - Method in class com.bayesserver.analysis.AutoInsightOutput: Contains the insights from each test variable.
getVariables() - Method in class com.bayesserver.Network: The collection of variables in the Bayesian network.
getVariables() - Method in class com.bayesserver.Node: Collection of variables represented by the node.
getVariables(Variable[]) - Method in class com.bayesserver.inference.DefaultEvidence: Fills out a buffer with all variables that have either hard or soft evidence.
getVariables(Variable[]) - Method in interface com.bayesserver.inference.Evidence: Fills out a buffer with all variables that have either hard or soft evidence.
getVariance() - Method in class com.bayesserver.statistics.IntervalStatistics: Gets the variance of the discretized variable.
getVariance(int, int) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getVariance(Variable) - Method in class com.bayesserver.CLGaussian: Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
getVariance(VariableContext, State...) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, State...) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, Integer) - Method in class com.bayesserver.CLGaussian: Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
getVariance(Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVariance(Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
getVarianceActual() - Method in class com.bayesserver.analysis.RegressionStatistics: Gets the variance of the actual column.
getVarianceActual() - Method in class com.bayesserver.data.R2CrossValidationTestResult: Gets the variance of the actual column values (as opposed to the predicted values).
getWarnings() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput: Contains any warnings generated by optimization algorithms.
getWarnings() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput: Contains any warnings generated by optimization algorithms.
getWarnings() - Method in interface com.bayesserver.optimization.OptimizerOutput: Contains any warnings generated by optimization algorithms.
getWeight() - Method in class com.bayesserver.data.discovery.WeightedValue: Gets the weight (support) for the WeightedValue.getValue().
getWeight() - Method in class com.bayesserver.data.ReadInfo: The case weight.
getWeight() - Method in class com.bayesserver.inference.DefaultEvidence: Gets a weight that can be applied to the evidence.
getWeight() - Method in interface com.bayesserver.inference.Evidence: Gets a weight that can be applied to the evidence.
getWeight(int, int, int) - Method in class com.bayesserver.CLGaussian: Gets the weight (regression coefficient) of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
getWeight(VariableContext, VariableContext, State...) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(VariableContext, VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(VariableContext, VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Variable) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
getWeight(Variable, Variable, State...) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Variable, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Variable, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Integer, Variable, Integer) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
getWeight(Variable, Integer, Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Integer, Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeight(Variable, Integer, Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian: Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
getWeightColumn() - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions: Gets a column that contains case weights for each record.
getWeightColumn() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets the name of a column which contains a weight (support) for each case.
getWeightColumn() - Method in class com.bayesserver.data.ReaderOptions: The name of the case weight column, if one is present.
getWeighted() - Method in class com.bayesserver.data.discovery.VariableInfoCount: The sum of record weights.
getWeighted() - Method in class com.bayesserver.data.discovery.VariableInfoValue: Gets the weighted value.
getWeightedCaseCount() - Method in interface com.bayesserver.data.CrossValidationTestResult: Gets the number of records in the test partitioning.
getWeightedCaseCount() - Method in class com.bayesserver.data.DataProgressEventArgs: Gets the number of cases read so far.
getWeightedCaseCount() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult: Gets the number of records in the test partitioning.
getWeightedCaseCount() - Method in class com.bayesserver.data.DefaultEvidenceReader: Gets the number of cases (weighted) read so far.
getWeightedCaseCount() - Method in class com.bayesserver.data.R2CrossValidationTestResult: Gets the number of records in the test partitioning.
getWeightedCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput: Gets the weighted case count in the learning data.
getWidth() - Method in class com.bayesserver.Bounds: Gets the width of the element.
getWindowColumnName() - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions: Gets the name of the column which will contain the window identifier.
getWindowTimeColumnName() - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions: Gets the name of the column which will contain the window time.
getX() - Method in class com.bayesserver.analysis.AssociationPair: Gets the variable contexts in the first set.
getX() - Method in class com.bayesserver.analysis.LiftChartPoint: Gets the value on the x-axis.
getX() - Method in class com.bayesserver.Bounds: Gets the x-axis value of the left side of the element.
getY() - Method in class com.bayesserver.analysis.AssociationPair: Gets the varible contexts in the second set.
getY() - Method in class com.bayesserver.analysis.LiftChartPoint: Gets the value on the y-axis.
getY() - Method in class com.bayesserver.Bounds: Gets the y-axis value of the top side of the element.

H

HARD - com.bayesserver.inference.EvidenceType: The value for the variable is known, such as the specific state of a discrete node.
HARD - com.bayesserver.optimization.DesignEvidenceKind: Evidence is set on a single discrete state.
hashCode() - Method in class com.bayesserver.Bounds
hashCode() - Method in class com.bayesserver.causal.CausalNode
hashCode() - Method in class com.bayesserver.data.discovery.WeightedValue
hashCode() - Method in class com.bayesserver.inference.EvidenceTypes
hashCode() - Method in class com.bayesserver.Interval
hashCode() - Method in class com.bayesserver.NodeDistributionKey
hashCode() - Method in class com.bayesserver.StateContext
hashCode() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
hashCode() - Method in class com.bayesserver.ValidationOptions
HEAD - com.bayesserver.HeadTail: Indicates that a variable is marked as head in a distribution.
HeadTail - Enum in com.bayesserver: Indicates whether a variable is marked as head or tail in a distribution.
HierarchicalLinkOutput - Class in com.bayesserver.learning.structure: Contains information about a new link learnt using the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning algorithm.
HierarchicalStructuralLearning - Class in com.bayesserver.learning.structure: A structural learning algorithm for Bayesian networks that groups subsets of nodes into a hierarchy.
HierarchicalStructuralLearning() - Constructor for class com.bayesserver.learning.structure.HierarchicalStructuralLearning
HierarchicalStructuralLearningOptions - Class in com.bayesserver.learning.structure: Options for structural learning with the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning class.
HierarchicalStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
HierarchicalStructuralLearningOutput - Class in com.bayesserver.learning.structure: Contains information returned from the com.bayesserver.learning.structure.hierarchical.HierarchicalStructuralLearning algorithm.
HierarchicalStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure: Progress information returned from the Hierarchical structural learning algorithm.
HistogramDensity - Class in com.bayesserver.analysis: Represents an empirical density function built from a histogram, which can represent arbitrary univariate distributions.
HistogramDensity(List<Interval<Double>>, List<Double>) - Constructor for class com.bayesserver.analysis.HistogramDensity: Constructs an empirical density function.
HistogramDensityItem - Class in com.bayesserver.analysis: Information about each interval in the histogram density.
HistogramDensityOptions - Class in com.bayesserver.analysis: Options for learning a histogram based empirical density.
HistogramDensityOptions() - Constructor for class com.bayesserver.analysis.HistogramDensityOptions

I

Identification - Interface in com.bayesserver.causal: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOptions - Interface in com.bayesserver.causal: Options for classes that implement Identification
IdentificationOutput - Interface in com.bayesserver.causal: Output for classes that implement Identification
identify(Evidence, Distribution, IdentificationOptions) - Method in class com.bayesserver.causal.BackdoorCriterion: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(Evidence, Distribution, IdentificationOptions) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(Evidence, Distribution, IdentificationOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(Evidence, Distribution, IdentificationOptions) - Method in interface com.bayesserver.causal.Identification: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(List<CausalNode>, List<CausalNode>, List<CausalNode>, IdentificationOptions) - Method in class com.bayesserver.causal.BackdoorCriterion: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(List<CausalNode>, List<CausalNode>, List<CausalNode>, IdentificationOptions) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(List<CausalNode>, List<CausalNode>, List<CausalNode>, IdentificationOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identify(List<CausalNode>, List<CausalNode>, List<CausalNode>, IdentificationOptions) - Method in interface com.bayesserver.causal.Identification: Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
identifyXZ(Evidence, FrontDoorSet, BackdoorCriterionOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).
identifyXZ(List<CausalNode>, FrontDoorSet, List<CausalNode>, BackdoorCriterionOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).
identifyZY(FrontDoorSet, List<CausalNode>, List<CausalNode>, BackdoorCriterionOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion
identifyZY(Evidence, FrontDoorSet, Distribution, BackdoorCriterionOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Uses the 'Backdoor criterion' to identify any 'adjustment sets' between front-door nodes (Z) and outcomes (Y).
IGNORE - com.bayesserver.learning.structure.LinkConstraintFailureMode: If the link constraint cannot be honoured, ignore and continue.
Impact - Class in com.bayesserver.analysis: Analyzes the impact of evidence.
ImpactAction - Interface in com.bayesserver.analysis: Interface to receive impact outputs from the Impact.calculate(com.bayesserver.Network, com.bayesserver.Variable, com.bayesserver.State, com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.ImpactOptions) method.
ImpactHypothesisOutput - Class in com.bayesserver.analysis: Output information about the hypothesis variable/state from an Impact analysis.
ImpactOptions - Class in com.bayesserver.analysis: Options affecting how Impact analysis calculations are performed.
ImpactOptions() - Constructor for class com.bayesserver.analysis.ImpactOptions
ImpactOutput - Class in com.bayesserver.analysis: Contains the results of an Impact analysis.
ImpactOutputItem - Class in com.bayesserver.analysis: The output from an impact analysis, for a particular subset of evidence.
ImpactSubsetMethod - Enum in com.bayesserver.analysis: Determines how subsets are determined during impact analysis.
include(DataRecord) - Method in interface com.bayesserver.data.DataReaderFilter: Determines whether a record should be included or not.
include(DataRecord) - Method in class com.bayesserver.data.PartitionDataReaderFilter: Determines whether a record should be included or not.
INCLUDE - com.bayesserver.analysis.ImpactSubsetMethod: The maximum subset size is the maximum size of the subset of evidence being analyzed.
INCLUDE - com.bayesserver.analysis.LogLikelihoodAnalysisSubsetMethod: The maximum subset size is the maximum size of the subset of evidence being analyzed.
INCLUDE_PARTITION_DATA - com.bayesserver.data.DataPartitionMethod: The data set should include data from the partition.
InconsistentEvidenceException - Exception in com.bayesserver.inference: Exception raised when either inconsistent evidence is detected, or underflow has occurred.
InconsistentEvidenceException() - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException: Initializes a new instance of the InconsistentEvidenceException class.
InconsistentEvidenceException(String) - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException: Initializes a new instance of the InconsistentEvidenceException class.
InconsistentEvidenceException(String, Throwable) - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException: Initializes a new instance of the InconsistentEvidenceException class with a specified error message and a reference to the inner exception that is the cause of this exception.
InconsistentEvidenceException(Throwable) - Constructor for exception com.bayesserver.inference.InconsistentEvidenceException: Initializes a new instance of the InconsistentEvidenceException class a reference to the inner exception that is the cause of this exception.
InconsistentEvidenceMode - Enum in com.bayesserver.inference: Determines when an InconsistentEvidenceException is raied.
increment() - Method in class com.bayesserver.TableIterator: Moves the iterator to the next value, with respect to the TableIterator node order.
IndependenceOptions - Class in com.bayesserver.learning.structure: Options governing independence and conditional independence tests.
IndependenceOptions() - Constructor for class com.bayesserver.learning.structure.IndependenceOptions
INDEX - com.bayesserver.data.ColumnValueType: The column contains the zero based index of a discrete variable state.
INDEX - com.bayesserver.data.TimeValueType: The time column contains zero based time indexes.
indexOf(Variable) - Method in class com.bayesserver.VariableContextCollection: Determines the index of a specific Variable in the collection.
indexOf(VariableContext, boolean) - Method in class com.bayesserver.VariableContextCollection: Determines the index of a specific variable-time combination in the collection.
indexOf(Variable, Integer) - Method in class com.bayesserver.VariableContextCollection: Determines the index of a specific Variable in the collection at the specified [time].
indexOf(Object) - Method in class com.bayesserver.NetworkLinkCollection: Determines the index of a specific Link in the collection.
indexOf(Object) - Method in class com.bayesserver.NetworkNodeCollection: Determines the index of a specific Node in the collection.
indexOf(Object) - Method in class com.bayesserver.NetworkVariableCollection: Determines the index of a specific Variable in the collection.
indexOf(Object) - Method in class com.bayesserver.NodeGroupCollection: Determines the index of a specific group in the collection.
indexOf(Object) - Method in class com.bayesserver.NodeVariableCollection: Determines the index of a specific Variable in the collection.
indexOf(Object) - Method in class com.bayesserver.StateCollection: Determines the index of a specific State in the collection.
indexOf(String) - Method in class com.bayesserver.data.DataColumnCollection: Gets the index of the column with the given name.
IndirectGraph - Class in com.bayesserver.causal: Methods for constructing the 'Indirect graph' from a Bayesian network.
IndirectGraphOptions - Class in com.bayesserver.causal: Options for 'Indirect graph' construction.
IndirectGraphOptions() - Constructor for class com.bayesserver.causal.IndirectGraphOptions
Inference - Interface in com.bayesserver.inference: The interface for a Bayesian network inference algorithm, which is used to perform queries such as calculating posterior probabilities and log-likelihood values for a case.
InferenceFactory - Interface in com.bayesserver.inference: Uses the factory design pattern to create inference related objects for inference algorithms.
INITIAL - com.bayesserver.TemporalType: A node which cannot link to temporal nodes at time t > 0.
InitializationMethod - Enum in com.bayesserver.learning.parameters: Determines the algorithm used to initialize distributions during parameter learning.
InitializationOptions - Class in com.bayesserver.learning.parameters: Options governing the initialization of distributions at the start of parameter learning.
InSampleAnomalyDetection - Class in com.bayesserver.analysis: Detects in-sample anomalies in a data set.
InSampleAnomalyDetectionActions - Interface in com.bayesserver.analysis: Actions which the caller must implement to use InSampleAnomalyDetection.
InSampleAnomalyDetectionOptions - Class in com.bayesserver.analysis: Options used by InSampleAnomalyDetection.
InSampleAnomalyDetectionOptions() - Constructor for class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
InSampleAnomalyDetectionOutput - Class in com.bayesserver.analysis: Output used by InSampleAnomalyDetection.
INSERT - com.bayesserver.CollectionAction: Specifies that an element was added to the collection.
instantiate(Variable, double) - Method in class com.bayesserver.CLGaussian: Calculates the distribution which results from instantiating a particular variable.
instantiate(Variable, double, Integer) - Method in class com.bayesserver.CLGaussian: Calculates the distribution which results from instantiating a particular variable at a specified time.
instantiate(Double[]) - Method in class com.bayesserver.CLGaussian: Calculates the distribution which results from instantiating a number of variables.
instantiate(Double[]) - Method in interface com.bayesserver.Distribution: Calculates the distribution which results from instantiating a number of variables.
instantiate(Double[]) - Method in class com.bayesserver.Table: Creates a table with a subset of variables by setting hard evidence on one or more variables.
instantiate(Integer[]) - Method in class com.bayesserver.Table: Creates a table with a subset of variables by setting hard evidence on one or more variables.
instantiateDiscrete(Integer[]) - Method in class com.bayesserver.CLGaussian: Instantiates discrete variables.
instantiateHead(double[], double[]) - Method in class com.bayesserver.CLGaussian: Instantiates all continuous head variable contexts.
instantiateHead(Variable, double, Integer) - Method in class com.bayesserver.CLGaussian: Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
instantiateHead(Variable, double, Integer, double[]) - Method in class com.bayesserver.CLGaussian: Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
instantiateHeads(Double[], double[]) - Method in class com.bayesserver.CLGaussian: Instantiates continuous head variable contexts.
instantiateTails(Double[]) - Method in class com.bayesserver.CLGaussian: Calculates the distribution which results from instantiating continuous tail variables.
INT - com.bayesserver.ExpressionReturnType: Expression returns a 32 bit integer value.
INTEGER - com.bayesserver.StateValueType: A State can have a integer value.
Interval<T extends Comparable> - Class in com.bayesserver: An interval, defined by a minimum and maximum with respective open or closed endpoints.
Interval() - Constructor for class com.bayesserver.Interval
Interval(T, T, IntervalEndPoint, IntervalEndPoint) - Constructor for class com.bayesserver.Interval: Initializes a new instance of an Interval.
IntervalEndPoint - Enum in com.bayesserver: The type of end point for an interval.
IntervalStatistics - Class in com.bayesserver.statistics: Calculates statistics such as mean and variance for discretized variables, i.e.
InterventionType - Enum in com.bayesserver.inference: Determines whether evidence is an intervention (do operator) or not.
invalidate() - Static method in class com.bayesserver.License: Resets any previous validation.
InvalidNetworkException - Exception in com.bayesserver: Raised when a network has not been correctly specified.
InvalidNetworkException() - Constructor for exception com.bayesserver.InvalidNetworkException: Initializes a new instance of the InvalidNetworkException class.
InvalidNetworkException(String) - Constructor for exception com.bayesserver.InvalidNetworkException: Initializes a new instance of the InvalidNetworkException class with a specified error message.
InvalidNetworkException(String, Throwable) - Constructor for exception com.bayesserver.InvalidNetworkException: Initializes a new instance of the InvalidNetworkException class with a specified error message and a reference to the inner exception that is the cause of this exception.
InvalidNetworkException(Throwable) - Constructor for exception com.bayesserver.InvalidNetworkException: Initializes a new instance of the InvalidNetworkException class with a reference to the inner exception that is the cause of this exception.
inverseCdf(double) - Method in interface com.bayesserver.analysis.EmpiricalDensity: Calculates an approximate value for the inverse cumulative distribution function.
inverseCdf(double) - Method in class com.bayesserver.analysis.HistogramDensity: Calculates an approximate value for the inverse cumulative distribution function.
isAnomalous() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput: Determines whether the record is deemed anomalous.
isAnomalous(Double) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput: Determines whether the record is deemed anomalous.
isDag() - Method in class com.bayesserver.Network: Determines whether this instance is a Directed Acyclic Graph (DAG) which is a requirement for Bayesian networks.
isDag(Network) - Static method in class com.bayesserver.Dag: Determines if a network is a Directed Acyclic Graph (DAG).
isDag(Network, Iterable<Link>, Iterable<Link>) - Static method in class com.bayesserver.Dag: Determines if a network is a DAG (Directed Acyclic Graph).
isFeature(double) - Method in class com.bayesserver.learning.structure.FeatureSelectionTest: Provides a hint as to whether the variable is likely to be a feature or not, at the given [significanceLevel].
isHead() - Method in class com.bayesserver.VariableContext: Determines whether this instance is marked as Head.
isNull(int) - Method in class com.bayesserver.data.DataReaderFiltered
isNull(int) - Method in interface com.bayesserver.data.DataRecord: Determines whether the value is null (missing) for the specified column.
isNull(int) - Method in class com.bayesserver.data.DataTableReader
isNull(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader: Determines whether the value is null (missing) for the specified column.
isReadOnly() - Method in class com.bayesserver.CLGaussian: Indicates whether the distribution is read only.
isReadOnly() - Method in interface com.bayesserver.Distribution: Indicates whether the distribution is read only.
isReadOnly() - Method in class com.bayesserver.Table: Indicates whether the distribution is read only.
isTail() - Method in class com.bayesserver.VariableContext: Determines whether this instance is marked as Tail.
isTree() - Method in class com.bayesserver.Network: Determines whether this instance is a tree (singly connected).
isValid(Evidence, Distribution, ValidationOptions) - Method in class com.bayesserver.causal.BackdoorCriterion: Tests whether adjustment inputs are valid, without raising an exception.
isValid(Evidence, Distribution, ValidationOptions) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: Tests whether adjustment inputs are valid, without raising an exception.
isValid(Evidence, Distribution, ValidationOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Tests whether adjustment inputs are valid, without raising an exception.
isValid(Evidence, Distribution, ValidationOptions) - Method in interface com.bayesserver.causal.Validation: Tests whether adjustment inputs are valid, without raising an exception.
isValid(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in class com.bayesserver.causal.BackdoorCriterion: Tests whether adjustment inputs are valid, without raising an exception.
isValid(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: Tests whether adjustment inputs are valid, without raising an exception.
isValid(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Tests whether adjustment inputs are valid, without raising an exception.
isValid(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in interface com.bayesserver.causal.Validation: Tests whether adjustment inputs are valid, without raising an exception.
iterate(VariableContextCollection[], int[], MultipleIterator.Combination) - Static method in class com.bayesserver.MultipleIterator: Iterates over all the variables and their states found in [subsets].
iterate(VariableContextCollection, VariableContextCollection[], int[], MultipleIterator.Combination) - Static method in class com.bayesserver.MultipleIterator: Iterates over all the variables and their states found in [subsets].

J

J_S_DIVERGENCE_BITS - com.bayesserver.inference.QueryDistance: Jensen Shannon divergence, JSD(P||Q) measured in BITS
JensenShannon - Class in com.bayesserver.statistics: Methods for computing the Jensen Shannon divergence, which measures the similarity between probability distributions.
JunctionTreeNodeDefinition - Interface in com.bayesserver.inference: A junction tree node, which can be either a clique or a sepset.
JunctionTreesDefinition - Class in com.bayesserver.inference: A jumction tree or junction trees.

K

K_L_DIVERGENCE - com.bayesserver.inference.QueryDistance: Kullback-Leibler divergence, D(P||Q) where Q is calculated using Base Evidence (or no evidence), and P is calculated from the standard evidence.
kFold(int, int, CrossValidationActions) - Static method in class com.bayesserver.data.CrossValidation: Performs k-fold cross validation.
kFoldList(int) - Static method in class com.bayesserver.data.CrossValidation: Gets a list of training and test DataPartitioning instances for each partition.
KullbackLeibler - Class in com.bayesserver.statistics: Calculate the Kullback–Leibler divergence between 2 distributions with the same variables, D(P||Q).

L

learn(DataPartitioning) - Method in interface com.bayesserver.data.CrossValidationActions: A user supplied function to learn a network based on a training partitioning of the data.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearning: Learn a cluster / mixture model.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.PCStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.SearchStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in interface com.bayesserver.learning.structure.StructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommandFactory, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.TANStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, ParameterLearningOptions) - Method in class com.bayesserver.learning.parameters.ParameterLearning: Learns the parameters of a Bayesian network or Dynamic Bayesian network, from data.
learn(EvidenceReaderCommand, List<DistributionSpecification>, ParameterLearningOptions) - Method in class com.bayesserver.learning.parameters.ParameterLearning: Learns the parameters of a Bayesian network or Dynamic Bayesian network, from data.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.PCStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.SearchStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in interface com.bayesserver.learning.structure.StructuralLearning: Learn the structure (links) of a Bayesian network.
learn(EvidenceReaderCommand, List<Node>, StructuralLearningOptions) - Method in class com.bayesserver.learning.structure.TANStructuralLearning: Learn the structure (links) of a Bayesian network.
learn(Network, EvidenceReaderCommand) - Method in interface com.bayesserver.analysis.ClusterCountActions: A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.
learn(Network, EvidenceReaderCommand) - Method in interface com.bayesserver.analysis.InSampleAnomalyDetectionActions: A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.
learn(Network, EvidenceReaderCommandFactory, InSampleAnomalyDetectionActions, InSampleAnomalyDetectionOptions) - Static method in class com.bayesserver.analysis.InSampleAnomalyDetection: Build the in-sample anomaly detector, which can be used to remove anomalous data from a training data set.
learn(Iterable<WeightedValue>, HistogramDensityOptions) - Static method in class com.bayesserver.analysis.HistogramDensity: Learns a univariate empirical density from data.
learnDistributed(Network, ParameterLearningOptions, Distributer<DistributerContext>) - Static method in class com.bayesserver.learning.parameters.ParameterLearning: Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
learnDistributed(Network, List<DistributionSpecification>, ParameterLearningOptions, Distributer<DistributerContext>) - Static method in class com.bayesserver.learning.parameters.ParameterLearning: Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
learnDistributedMapper(EvidencePartition<DistributedMapperContext>, NameValuesReader, NameValuesWriter, InferenceFactory) - Static method in class com.bayesserver.learning.parameters.ParameterLearning: This method should be called during distributed parameter learning on a distributed partition.
learnDistributedReducer(Iterable<NameValuesReader>, NameValuesReader, NameValuesWriter) - Static method in class com.bayesserver.learning.parameters.ParameterLearning: Aggregates (reduces) the results obtained from the distributed calls to ParameterLearning.learnDistributedMapper(com.bayesserver.data.distributed.EvidencePartition<com.bayesserver.learning.parameters.DistributedMapperContext>, com.bayesserver.NameValuesReader, com.bayesserver.NameValuesWriter, com.bayesserver.inference.InferenceFactory).
License - Class in com.bayesserver: Provides license validation.
LIFT - com.bayesserver.inference.QueryComparison: Each queried value is divided by its value when calculated using Base evidence.
LiftChart - Class in com.bayesserver.analysis: Represents a lift chart, used to measure predictive performance.
LiftChartPoint - Class in com.bayesserver.analysis: Represents an XY coordinate in a lift chart.
LiftChartPoint() - Constructor for class com.bayesserver.analysis.LiftChartPoint
LikelihoodSamplingInference - Class in com.bayesserver.inference: An approximate probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, based on Likelihood Sampling.
LikelihoodSamplingInference(Network) - Constructor for class com.bayesserver.inference.LikelihoodSamplingInference: Initializes a new instance of the LikelihoodSamplingInference class, with the target Bayesian network.
LikelihoodSamplingInferenceFactory - Class in com.bayesserver.inference: Uses the factory design pattern to create inference related objects for the Likelihood Sampling algorithm.
LikelihoodSamplingInferenceFactory() - Constructor for class com.bayesserver.inference.LikelihoodSamplingInferenceFactory
LikelihoodSamplingQueryLifecycleBegin - Class in com.bayesserver.inference: Query lifecycle begin implementation for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryLifecycleEnd - Class in com.bayesserver.inference: Query end lifecycle implementation for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryOptions - Class in com.bayesserver.inference: Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
LikelihoodSamplingQueryOptions() - Constructor for class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Initializes a new instance of the LikelihoodSamplingQueryOptions class.
LikelihoodSamplingQueryOutput - Class in com.bayesserver.inference: Returns any information, in addition to the distributions, that is requested from a query.
LikelihoodSamplingQueryOutput() - Constructor for class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Initializes a new instance of the LikelihoodSamplingQueryOutput class.
Link - Class in com.bayesserver: Represents a directed link in a Bayesian network.
Link(Node, Node) - Constructor for class com.bayesserver.Link: Initializes a new instance of the Link class with the parent node specified in [from] and the child in [to].
Link(Node, Node, int) - Constructor for class com.bayesserver.Link: Initializes a new instance of the Link class with a specified [temporalOrder], the parent node specified in [from] and the child in [to].
linkCollectionChange(int, Link, Link, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
LinkConstraint - Class in com.bayesserver.learning.structure: Defines a constraint on a link between two nodes during structural learning.
LinkConstraint(Node, Node, LinkConstraintMethod, LinkConstraintFailureMode) - Constructor for class com.bayesserver.learning.structure.LinkConstraint: Initializes a new instance of the LinkConstraint class.
LinkConstraint(Node, Node, Integer, LinkConstraintMethod, LinkConstraintFailureMode) - Constructor for class com.bayesserver.learning.structure.LinkConstraint: Initializes a new instance of the LinkConstraint class.
LinkConstraintCollection - Class in com.bayesserver.learning.structure: A collection of link constraints.
LinkConstraintCollection() - Constructor for class com.bayesserver.learning.structure.LinkConstraintCollection
LinkConstraintFailureMode - Enum in com.bayesserver.learning.structure: Determines the action taken if a link constraint cannot be honoured.
LinkConstraintMethod - Enum in com.bayesserver.learning.structure: Determines how a link is constrained.
LinkOutput - Interface in com.bayesserver.learning.structure: Contains information about a link returned from a structural learning algorithm.
LIST_MINIMUM_SEPARATORS - com.bayesserver.causal.BackdoorMethod: Generates a list of adjustment sets that are minimal.
load(InputStream) - Method in class com.bayesserver.inference.DefaultEvidence: Loads evidence from the specified stream.
load(InputStream) - Method in interface com.bayesserver.inference.Evidence: Loads evidence from the specified stream.
load(InputStream) - Method in class com.bayesserver.Network: Loads a Network from the specified input InputStream.
load(String) - Method in class com.bayesserver.inference.DefaultEvidence: Loads evidence from the specified file.
load(String) - Method in interface com.bayesserver.inference.Evidence: Loads evidence from the specified file.
load(String) - Method in class com.bayesserver.Network: Loads a Network from the specified [path].
loadFromString(String) - Method in class com.bayesserver.inference.DefaultEvidence: Loads evidence from a string using UTF-8 encoding.
loadFromString(String) - Method in interface com.bayesserver.inference.Evidence: Loads evidence from a string using UTF-8 encoding.
loadFromString(String) - Method in class com.bayesserver.Network: Loads a network from a string using UTF-8 encoding.
loadFromString(String, String) - Method in class com.bayesserver.inference.DefaultEvidence: Loads evidence from a string using the specified encoding.
loadFromString(String, String) - Method in interface com.bayesserver.inference.Evidence: Loads evidence from a string using the specified encoding.
loadFromString(String, String) - Method in class com.bayesserver.Network: Loads a network from a string using the specified encoding.
LOG_LIKELIHOOD - com.bayesserver.learning.parameters.ConvergenceMethod: Convergence is determined based on the change in the log-likelihood between successive iterations.
LogarithmBase - Enum in com.bayesserver.statistics: Determines the base of the logarithm to use during calculations such as mutual information.
LogLikelihoodAnalysis - Class in com.bayesserver.analysis: Analyzes the log-likelihood for different evidence subsets.
LogLikelihoodAnalysisAction - Interface in com.bayesserver.analysis: Interface to receive Log-Likelihood analysis outputs from the LogLikelihoodAnalysis.calculate(com.bayesserver.Network, com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, com.bayesserver.analysis.LogLikelihoodAnalysisOptions) method.
LogLikelihoodAnalysisBaselineOutput - Class in com.bayesserver.analysis: Output information about the log-likelihood from a log-likelihood analysis.
LogLikelihoodAnalysisOptions - Class in com.bayesserver.analysis: Options affecting how Log-Likelihood analysis calculations are performed.
LogLikelihoodAnalysisOptions() - Constructor for class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
LogLikelihoodAnalysisOutput - Class in com.bayesserver.analysis: Contains the results of a Log-Likelihood analysis.
LogLikelihoodAnalysisOutputItem - Class in com.bayesserver.analysis: The output from a Log-Likelihood analysis, for a particular subset of evidence.
LogLikelihoodAnalysisSubsetMethod - Enum in com.bayesserver.analysis: Determines how subsets are determined during a Log-Likelihood analysis.
LoopyBeliefInference - Class in com.bayesserver.inference: An approximate but deterministic probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks based on Loopy Belief Propagation.
LoopyBeliefInference(Network) - Constructor for class com.bayesserver.inference.LoopyBeliefInference: Initializes a new instance of the LoopyBeliefInference class, with the target Bayesian network.
LoopyBeliefInferenceFactory - Class in com.bayesserver.inference: Uses the factory design pattern to create inference related objects for the Loopy Belief algorithm.
LoopyBeliefInferenceFactory() - Constructor for class com.bayesserver.inference.LoopyBeliefInferenceFactory
LoopyBeliefQueryLifecycleBegin - Class in com.bayesserver.inference: Query lifecycle begin implementation for the Loopy Belief algorithm.
LoopyBeliefQueryLifecycleEnd - Class in com.bayesserver.inference: Query end lifecycle implementation for the Loopy Belief algorithm.
LoopyBeliefQueryOptions - Class in com.bayesserver.inference: Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
LoopyBeliefQueryOptions() - Constructor for class com.bayesserver.inference.LoopyBeliefQueryOptions: Initializes a new instance of the LoopyBeliefQueryOptions class.
LoopyBeliefQueryOutput - Class in com.bayesserver.inference: Returns any information, in addition to the distributions, that is requested from a query.
LoopyBeliefQueryOutput() - Constructor for class com.bayesserver.inference.LoopyBeliefQueryOutput: Initializes a new instance of the LoopyBeliefQueryOutput class.

M

M_E_U - com.bayesserver.analysis.ValueOfInformationKind: The maximum expected utility of a decision node given the test and any other evidence,
marginalize(CLGaussian) - Method in class com.bayesserver.CLGaussian: Marginalizes (sums/integrates) the [superset] into this instance.
marginalize(Distribution) - Method in class com.bayesserver.CLGaussian: Marginalizes (integrates) the [superset] into this instance.
marginalize(Distribution) - Method in interface com.bayesserver.Distribution: Marginalizes (sums/integrates) the [superset] into this instance.
marginalize(Distribution) - Method in class com.bayesserver.Table: Marginalizes (sums) the [superset] into this instance.
marginalize(Distribution, PropagationMethod) - Method in class com.bayesserver.CLGaussian: Marginalizes (integrates) the [superset] into this instance.
marginalize(Distribution, PropagationMethod) - Method in interface com.bayesserver.Distribution: Marginalizes (sums/integrates) the [superset] into this instance.
marginalize(Distribution, PropagationMethod) - Method in class com.bayesserver.Table: Marginalizes (sums) the [superset] into this instance.
marginalize(Table) - Method in class com.bayesserver.Table: Marginalizes (sums) the [superset] into this instance.
marginalize(Table, boolean) - Method in class com.bayesserver.Table: Marginalizes (sums) the [superset] into this instance.
marginalize(Table, boolean, PropagationMethod) - Method in class com.bayesserver.Table: Marginalizes (sums) the [superset] into this instance.
marginalize(Table, PropagationMethod) - Method in class com.bayesserver.Table: Marginalizes (sums) the [superset] into this instance.
marginalizeLowMemory(Table[]) - Method in class com.bayesserver.Table: Marginalizes (sums) the combined [tables], without requiring the memory for the combined distribution.
marginalizeLowMemory(Table[], Table.MarginalizeLowMemoryOptions) - Method in class com.bayesserver.Table: Marginalizes (sums) the combined [tables], without requiring the memory for the combined distribution.
MarginalizeLowMemoryOptions() - Constructor for class com.bayesserver.Table.MarginalizeLowMemoryOptions
marginalizeTo(Table) - Method in class com.bayesserver.CLGaussian: Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
marginalizeTo(Table, PropagationMethod) - Method in class com.bayesserver.CLGaussian: Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
MAX - com.bayesserver.PropagationMethod: Max propagation is used to determine the most probable configuration of discrete variables, given any evidence entered.
MAXIMIZE - com.bayesserver.optimization.ObjectiveKind: The optimization target should be maximized.
MINIMIZE - com.bayesserver.optimization.ObjectiveKind: The optimization target should be minimized.
MISSING_VALUE - com.bayesserver.data.EmptyStringAction: Assume the variable state is missing/unknown/null.
MISSING_VALUE - com.bayesserver.data.StateNotFoundAction: Assume the variable state is missing/unknown/null.
MultipleIterator - Class in com.bayesserver: Provides methods to iterate over multiple distributions.
MultipleIterator.Combination - Interface in com.bayesserver
multiply(CLGaussian) - Method in class com.bayesserver.CLGaussian: Multiplies this instance by another CLGaussian distribution.
multiply(Distribution) - Method in class com.bayesserver.CLGaussian: Multiplies this instance by another distribution.
multiply(Distribution) - Method in interface com.bayesserver.Distribution: Creates a new distribution which is the result of multiplying this instance by another distribution.
multiply(Distribution) - Method in class com.bayesserver.Table: Creates a new distribution by multiplying this instance by another distribution.
multiplyInPlace(double) - Method in class com.bayesserver.Table: Multiplies all values in the distribution by the specified value.
multiplyInPlace(Table) - Method in class com.bayesserver.Table: Multiplies the [subset] into this instance.
multiplyInPlace(Table, boolean) - Method in class com.bayesserver.Table: Multiplies the [subset] into this instance.
MUTUAL_INFORMATION - com.bayesserver.analysis.ValueOfInformationKind: The mutual information between the hypothesis and the test, given any evidence.
MutualInformation - Class in com.bayesserver.statistics: Calculates mutual information or conditional mutual information, which measures the dependence between two variables.

N

NAME - com.bayesserver.data.ColumnValueType: The column contains the name of a discrete variable state.
NameValuesReader - Interface in com.bayesserver: Interface for reading name/value pairs.
NameValuesWriter - Interface in com.bayesserver: Interface for writing name/value pairs.
NATURAL - com.bayesserver.statistics.LogarithmBase: Natural (base e) logarithm.
NestedDataReader - Class in com.bayesserver.data: Allows nested table to be read using a DefaultDataReader.
NestedDataReader(DataReader, String) - Constructor for class com.bayesserver.data.NestedDataReader: Initializes a new instance of the NestedDataReader class.
NestedReadInfo - Class in com.bayesserver.data: Provides information about a nested table record.
NestedReadInfo(DataRecord) - Constructor for class com.bayesserver.data.NestedReadInfo: Initializes a new instance of the NestedReadInfo class.
Network - Class in com.bayesserver: Represents a Bayesian Network, or a Dynamic Bayesian Network.
Network() - Constructor for class com.bayesserver.Network: Initializes a new instance of the Network class.
Network(String) - Constructor for class com.bayesserver.Network: Initializes a new instance of the Network class with the specified [name].
NetworkLinkCollection - Class in com.bayesserver: Represents the collection of directed links maintained by the Network class.
NetworkMonitor - Interface in com.bayesserver: For internal use.
NetworkNodeCollection - Class in com.bayesserver: Represents the collection of Network.getNodes() maintained by the Network class.
NetworkNodeGroupCollection - Class in com.bayesserver: A collection of groups.
NetworkVariableCollection - Class in com.bayesserver: Represents a read-only collection of variables that belong to a network.
newDistribution() - Method in class com.bayesserver.Node: Creates a new distribution suitable for the node, however does not assign it to the node's Node.getDistribution() property.
newDistribution(int) - Method in class com.bayesserver.Node: Creates a new distribution suitable for the requested temporal order, however it is not assigned to the node.
newDistribution(NodeDistributionKey) - Method in class com.bayesserver.Node: Creates a new distribution suitable for the requested temporal order/related node, however it is not assigned to the node.
newDistribution(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.Node: Creates a new distribution suitable for the requested temporal order/related node, however it is not assigned to the node.
newDistribution(NodeDistributionKey, NodeDistributionKind, DistributionExpression) - Method in class com.bayesserver.Node: Creates a new distribution from an expression suitable for the requested temporal order/related node, however it is not assigned to the node, and neither is the expression.
newDistribution(NodeDistributionKind) - Method in class com.bayesserver.Node: Creates a new distribution with the given kind, however it is not assigned to the node.
newRow() - Method in class com.bayesserver.data.DataTable: Creates a new row of data, but does not add it to the table.
nextDouble() - Method in class com.bayesserver.RandomDefault: Generates a random floating-point number that is greater than or equal to 0.0, and less than 1.0.
nextDouble() - Method in interface com.bayesserver.RandomNumberGenerator: Generates a random floating-point number that is greater than or equal to 0.0, and less than 1.0.
nextInt(int, int) - Method in class com.bayesserver.RandomDefault: Generates a random integer between [minValue..maxValue).
nextInt(int, int) - Method in interface com.bayesserver.RandomNumberGenerator: Generates a random integer between [minValue..maxValue).
Node - Class in com.bayesserver: Represents a node with one or more variables in a Bayesian network.
Node() - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class, with no variables, and no name.
Node(Variable) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class with a specified Variable and assigns the name of the variable to the node.
Node(String, int) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class with the specified [name] and automatically adds a discrete Variable with the number of states specified in [states].
Node(String, State...) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class, with the name of the node, automatically creating an associated discrete Variable and adds the states specified in [states] to that variable.
Node(String, Variable...) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class with a specified name and a number of variables.
Node(String, VariableValueType) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class with the specified [name].
Node(String, VariableValueType, VariableKind) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class with the specified [name].
Node(String, String[]) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class, with the name of the node, automatically creating an associated discrete Variable and adds the states specified in [states] to that variable.
Node(String, List<Variable>) - Constructor for class com.bayesserver.Node: Initializes a new instance of the Node class with a specified name and a number of variables.
nodeCollectionChange(int, Node, Node, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
NodeDistributionExpressions - Class in com.bayesserver: Represents any distribution expressions assigned to a Node.
NodeDistributionExpressions.DistributionExpressionOrder - Class in com.bayesserver: Identifies a distribution expression and its temporal order.
NodeDistributionKey - Class in com.bayesserver: Identifies a distribution assigned or to be assigned to a node.
NodeDistributionKey() - Constructor for class com.bayesserver.NodeDistributionKey: Initializes a new instance of the NodeDistributionKey class with defaults.
NodeDistributionKey(int) - Constructor for class com.bayesserver.NodeDistributionKey: Initializes a new instance of a NodeDistributionKey.
NodeDistributionKey(int, Node) - Constructor for class com.bayesserver.NodeDistributionKey: Initializes a new instance of a NodeDistributionKey.
NodeDistributionKey(Node) - Constructor for class com.bayesserver.NodeDistributionKey: Initializes a new instance of a NodeDistributionKey.
NodeDistributionKind - Enum in com.bayesserver: The kind of distribution, such as a standard Probability or Experience table.
NodeDistributionOptions - Class in com.bayesserver: Options that apply to all distributions of a particular node.
NodeDistributions - Class in com.bayesserver: Represents the distributions assigned to a Node.
NodeDistributions.DistributionOrder - Class in com.bayesserver: Identifies a distribution and its temporal order.
NodeGroup - Class in com.bayesserver: Allows nodes to be assigned to one or more groups.
NodeGroup(String) - Constructor for class com.bayesserver.NodeGroup: Initializes a new instance of the NodeGroup class.
NodeGroupCollection - Class in com.bayesserver: Represents the collection of groups a node belongs to.
NodeLinkCollection - Class in com.bayesserver: Represents a read-only collection of links.
NodeSet - Interface in com.bayesserver.causal: A set of nodes.
NodeSetItem - Interface in com.bayesserver.causal: Represents a node in a set.
NodeVariableCollection - Class in com.bayesserver: Represents the collection of variables belonging to a
NOISY_OR_MAX - com.bayesserver.NoisyType: The node is a noisy or/max node.
noisyNodeTypeChanged(Node, NoisyType, NoisyType) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
NoisyOrder - Enum in com.bayesserver: Determines the order in which the states of a parent of a noisy node increasingly affect the noisy states.
noisyOrderChanged(Link, NoisyOrder, NoisyOrder) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
NoisyType - Enum in com.bayesserver: Identifies the noisy node type, if any.
NONE - com.bayesserver.analysis.AutoInsightJSDivergence: Jensen Shannon divergence is not calculated during the auto-insight analysis.
NONE - com.bayesserver.analysis.AutoInsightKLDivergence: KL divergence is not calculated during the auto-insight analysis.
NONE - com.bayesserver.data.discovery.DiscretizationMethod: Do not discretize.
NONE - com.bayesserver.data.discovery.SortOrder: Discrete states should not be sorted.
NONE - com.bayesserver.ExpressionDistribution: Do not generate the distribution.
NONE - com.bayesserver.inference.DecisionAlgorithm: Any decision nodes are treated like standard probability nodes.
NONE - com.bayesserver.inference.EvidenceType: No evidence has been set.
NONE - com.bayesserver.inference.InterventionType: Standard evidence
NONE - com.bayesserver.inference.QueryComparison: No comparison is made.
NONE - com.bayesserver.inference.QueryDistance: No distance is calculated.
NONE - com.bayesserver.learning.parameters.DistributionMonitoring: Do not monitor any distributions.
NONE - com.bayesserver.learning.parameters.TimeSeriesMode: Not applicable.
NONE - com.bayesserver.NoisyType: The node is not a noisy node.
NONE - com.bayesserver.StateValueType: A State has no value.
NONE - com.bayesserver.TableExpressionNormalization: No normalization.
nonZero(Table.NonZeroValues) - Method in class com.bayesserver.Table: Returns any non zero table values, keyed by index.
normalize() - Method in class com.bayesserver.Table: Normalizes the distribution such that each parent combination sums to 1.
normalize(boolean) - Method in class com.bayesserver.Table: Normalizes the distribution such that each parent combination sums to 1.
NORMALIZE - com.bayesserver.TableExpressionNormalization: Normalize, but do not adjust any zero sum configurations.
NORMALIZE_UNIFY_ZERO_SUM - com.bayesserver.TableExpressionNormalization: Normalize, and set any zero sum configurations to a uniform distribution.
NOT_A_TO_B_OR_B_TO_A - com.bayesserver.learning.structure.LinkConstraintMethod: Ensures that a link is not created between A and B.
NOT_APPLICABLE - com.bayesserver.inference.InterventionType: Does not apply.
NOT_APPLICABLE - com.bayesserver.learning.parameters.DecisionPostProcessingMethod: There are no decision nodes in the network.
NotInDomainException - Exception in com.bayesserver: Raised when the arguments to a mathematic function are not in the domain of the function (undefined).
NotInDomainException() - Constructor for exception com.bayesserver.NotInDomainException: Initializes a new instance of the NotInDomainException class.
NotInDomainException(String) - Constructor for exception com.bayesserver.NotInDomainException: Initializes a new instance of the NotInDomainException class with a specified error message.
NotInDomainException(String, Throwable) - Constructor for exception com.bayesserver.NotInDomainException: Initializes a new instance of the NotInDomainException class with a specified error message and a reference to the inner exception that is the cause of this exception.
NotInDomainException(Throwable) - Constructor for exception com.bayesserver.NotInDomainException: Initializes a new instance of the NotInDomainException class with a reference to the inner exception that is the cause of this exception.
NotSpdException - Exception in com.bayesserver: Raised when a matrix is not positive definite.
NotSpdException() - Constructor for exception com.bayesserver.NotSpdException: Initializes a new instance of the NotSpdException class.
NotSpdException(String) - Constructor for exception com.bayesserver.NotSpdException: Initializes a new instance of the NotSpdException class with a specified error message.
NotSpdException(String, Throwable) - Constructor for exception com.bayesserver.NotSpdException: Initializes a new instance of the NotSpdException class with a specified error message and a reference to the inner exception that is the cause of this exception.
NotSpdException(Throwable) - Constructor for exception com.bayesserver.NotSpdException: Initializes a new instance of the NotSpdException class with a reference to the inner exception that is the cause of this exception.
numericValue() - Method in interface com.bayesserver.data.CrossValidationTestResult: Returns the test result as a numeric value if supported, otherwise returns null.
numericValue() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult: Returns the test result as a numeric value if supported, otherwise returns null.
numericValue() - Method in class com.bayesserver.data.R2CrossValidationTestResult: Returns the test result as a numeric value if supported, otherwise returns null.

O

Objective - Class in com.bayesserver.optimization: Defines the target variable or state that you wish to maximize or minimize.
Objective(State, ObjectiveKind) - Constructor for class com.bayesserver.optimization.Objective: Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.
Objective(State, ObjectiveKind, Double) - Constructor for class com.bayesserver.optimization.Objective: Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.
Objective(Variable, ObjectiveKind) - Constructor for class com.bayesserver.optimization.Objective: Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.
Objective(Variable, ObjectiveKind, Double) - Constructor for class com.bayesserver.optimization.Objective: Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.
ObjectiveKind - Enum in com.bayesserver.optimization: The type of optimization to carry out, such as Minimization or Maximization.
OBSERVABLE - com.bayesserver.CausalObservability: The causal node is observable.
oneWay(Evidence, State, ParameterReference) - Method in class com.bayesserver.analysis.SensitivityToParameters: Calculates how a hypothesis varies based on changes to a single parameter.
oneWayDifference(SensitivityFunctionOneWay, SensitivityFunctionOneWay, Interval<Double>) - Static method in class com.bayesserver.analysis.ParameterTuning: Given a pair of sensitivity functions (evaluated on the same parameter and evidence but different hypotheses), determines how the parameter under consideration can be altered so that the difference between the hypothesis probabilities P(h1|e) - P(h2|e) is within a given range.
oneWayRatio(SensitivityFunctionOneWay, SensitivityFunctionOneWay, Interval<Double>) - Static method in class com.bayesserver.analysis.ParameterTuning: Given a pair of sensitivity functions (evaluated on the same parameter and evidence but different hypotheses), determines how the parameter under consideration can be altered so that the ratio between the hypothesis probabilities P(h1|e) / P(h2|e) is within a given range.
oneWaySimple(SensitivityFunctionOneWay, Interval<Double>) - Static method in class com.bayesserver.analysis.ParameterTuning: Given a sensitivity function, determines how the parameter under consideration can be altered so that the resulting value of the hypothesis is within a given range.
OnlineLearning - Class in com.bayesserver.learning.parameters: Adapts the parameters of a Bayesian network, using Bayesian statistics.
OnlineLearning(Network, InferenceFactory) - Constructor for class com.bayesserver.learning.parameters.OnlineLearning: Initializes a new instance of the OnlineLearning class.
OnlineLearningOptions - Class in com.bayesserver.learning.parameters: Options for online learning (adaptation using Bayesian statistics).
OnlineLearningOptions() - Constructor for class com.bayesserver.learning.parameters.OnlineLearningOptions
OPEN - com.bayesserver.IntervalEndPoint: The end point of an interval is open.
OptimizationWarning - Class in com.bayesserver.optimization: A warning generated by an optimization algorithm
OptimizationWarning(String) - Constructor for class com.bayesserver.optimization.OptimizationWarning: Initializes a new instance of the OptimizationWarning class.
optimize(Network, Objective, List<DesignVariable>, Evidence, OptimizerOptions) - Method in class com.bayesserver.optimization.GeneticOptimizer: Perform optimization of an objective (target).
optimize(Network, Objective, List<DesignVariable>, Evidence, OptimizerOptions) - Method in class com.bayesserver.optimization.GeneticSimplification: Perform optimization of an objective (target).
optimize(Network, Objective, List<DesignVariable>, Evidence, OptimizerOptions) - Method in interface com.bayesserver.optimization.Optimizer: Perform optimization of an objective (target).
Optimizer - Interface in com.bayesserver.optimization: Interface required by optimization algorithms.
OptimizerOptions - Interface in com.bayesserver.optimization: Optimizer options that are common across all algorithms.
OptimizerOutput - Interface in com.bayesserver.optimization: Contains output common to optimization algorithms.
OptimizerProgress - Interface in com.bayesserver.optimization: Interface to provide progress information during optimization.
OptimizerProgressInfo - Interface in com.bayesserver.optimization: Interface to provide progress information during optimization.

P

ParameterCounter - Class in com.bayesserver: Contains methods to determine the number of parameters in a Bayesian network or distribution.
ParameterCountOptions - Class in com.bayesserver: Options for ParameterCounter.
ParameterCountOptions() - Constructor for class com.bayesserver.ParameterCountOptions
ParameterLearning - Class in com.bayesserver.learning.parameters: Learns the parameters of Bayesian networks and Dynamic Bayesian networks, from data.
ParameterLearning(Network, InferenceFactory) - Constructor for class com.bayesserver.learning.parameters.ParameterLearning: Initializes a new instance of the ParameterLearning class.
ParameterLearningOptions - Class in com.bayesserver.learning.parameters: Options governing parameter learning.
ParameterLearningOptions() - Constructor for class com.bayesserver.learning.parameters.ParameterLearningOptions
ParameterLearningOutput - Class in com.bayesserver.learning.parameters: Contains summary information returned by ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
ParameterLearningProgress - Interface in com.bayesserver.learning.parameters: Interface to provide progress information during parameter learning.
ParameterLearningProgressInfo - Class in com.bayesserver.learning.parameters: Provides progress information during ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
ParameterReference - Class in com.bayesserver.analysis: References a parameter in a node distribution.
ParameterReference(Node, NodeDistributionKey, State[]) - Constructor for class com.bayesserver.analysis.ParameterReference: Initializes a new instance of the ParameterReference class .
ParameterReference(Node, State[]) - Constructor for class com.bayesserver.analysis.ParameterReference: Initializes a new instance of the ParameterReference class.
PARAMETERS - com.bayesserver.learning.parameters.ConvergenceMethod: Convergence is determined based on the change in network parameters between successive iterations.
ParameterTuning - Class in com.bayesserver.analysis: Calculates how a parameter can be updated so that the resulting value of a hypothesis is within a given range.
ParameterTuningOneWay - Class in com.bayesserver.analysis: Represents the result of one way parameter tuning.
PartitionDataReaderFilter - Class in com.bayesserver.data: A data reader filter based on an integer column, which can contain ids or a zero based partition identifier.
PartitionDataReaderFilter(DataPartitioning, int, String) - Constructor for class com.bayesserver.data.PartitionDataReaderFilter: Initializes a new instance of the PartitionDataReaderFilter class.
PCLinkOutput - Class in com.bayesserver.learning.structure: Contains information about a new link learnt using the com.bayesserver.learning.structure.pc.PCStructuralLearning algorithm.
PCStructuralLearning - Class in com.bayesserver.learning.structure: A structural learning algorithm for Bayesian networks based on the PC algorithm.
PCStructuralLearning() - Constructor for class com.bayesserver.learning.structure.PCStructuralLearning
PCStructuralLearningOptions - Class in com.bayesserver.learning.structure: Options for structural learning with the com.bayesserver.learning.structure.pc.PCStructuralLearning class.
PCStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.PCStructuralLearningOptions: Initializes a new instance of the com.bayesserver.learning.structure.pc.PCStructuralLearningOptions class.
PCStructuralLearningOutput - Class in com.bayesserver.learning.structure: Contains information returned from the com.bayesserver.learning.structure.pc.PCStructuralLearning algorithm.
PCStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure: Progress information returned from the PC structural learning algorithm.
PINNED - com.bayesserver.learning.parameters.TimeSeriesMode: At each time point, lower order distributions are only updated until a higher order distribution can be used.
Priors - Class in com.bayesserver.learning.parameters: Contains parameters used to avoid boundary conditions during learning.
PROBABILITIES - com.bayesserver.learning.parameters.DecisionPostProcessingMethod: The distributions learned from the data are left intact and not overridden.
PROBABILITY - com.bayesserver.NodeDistributionKind: The standard kind of probability distribution found in Bayesian networks.
PROBABILITY - com.bayesserver.VariableKind: A standard probability variable.
PropagationMethod - Enum in com.bayesserver: The propagation method used during inference.

Q

query(QueryOptions, QueryOutput) - Method in class com.bayesserver.causal.CausalInferenceBase: Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in interface com.bayesserver.inference.Inference: Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.LoopyBeliefInference: Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.RelevanceTreeInference: Calculates a number of distributions, e.g.
query(QueryOptions, QueryOutput) - Method in class com.bayesserver.inference.VariableEliminationInference: Calculates a number of distributions, e.g.
query(Network, QueryDistributionCollection, Evidence, TreeQueryOptions) - Static method in class com.bayesserver.inference.TreeQuery: Calculates properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
QUERY_OPTIMIZED - com.bayesserver.inference.InconsistentEvidenceMode: This is the default mode, and only raises an exception, when the requested queries cannot be computed.
QueryComparison - Enum in com.bayesserver.inference: Determines whether and how queried values (e.g.
QueryDistance - Enum in com.bayesserver.inference: Type of distance to calculate for a query.
QueryDistribution - Class in com.bayesserver.inference: Defines a distribution to be queried in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryDistribution(Distribution) - Constructor for class com.bayesserver.inference.QueryDistribution: Initializes a new instance of the QueryDistribution class.
QueryDistribution(Distribution, boolean) - Constructor for class com.bayesserver.inference.QueryDistribution: Initializes a new instance of the QueryDistribution class.
QueryDistributionCollection - Interface in com.bayesserver.inference: The collection of distributions to be calculated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
queryDistributionsInner(QueryOptions, QueryOutput) - Method in class com.bayesserver.causal.BackdoorInference
queryDistributionsInner(QueryOptions, QueryOutput) - Method in class com.bayesserver.causal.CausalInferenceBase
queryDistributionsInner(QueryOptions, QueryOutput) - Method in class com.bayesserver.causal.DisjunctiveCauseInference
queryDistributionsInner(QueryOptions, QueryOutput) - Method in class com.bayesserver.causal.FrontDoorInference
QueryEvidenceMode - Enum in com.bayesserver.inference: Determines how predictions on variables with evidence are performed.
QueryExpression - Interface in com.bayesserver: Base interface for expressions that are evaluated at query time.
QueryFunction - Class in com.bayesserver.inference: Defines a function to be evaluated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryFunction(QueryFunctionOutput) - Constructor for class com.bayesserver.inference.QueryFunction: Initializes a new instance of the QueryFunction class.
QueryFunction(QueryFunctionOutput, boolean) - Constructor for class com.bayesserver.inference.QueryFunction: Initializes a new instance of the QueryFunction class.
QueryFunctionCollection - Interface in com.bayesserver.inference: Collection of functions to be evaluated at query time, after any query distributions have been calculated.
QueryFunctionOutput - Class in com.bayesserver.inference: A class whose value holds the result of a function evaluation, populated during a query.
QueryFunctionOutput(Variable) - Constructor for class com.bayesserver.inference.QueryFunctionOutput: Initializes a new instance of the com.bayesserver.QueryFunctionOutput class.
QueryLifecycle - Interface in com.bayesserver.inference: Allows callers to hook into the query lifecycle of an inference engine.
QueryLifecycleBegin - Interface in com.bayesserver.inference: Contains information that is passed via the QueryLifecycle interface.
QueryLifecycleBeginBase - Class in com.bayesserver.inference: Query begin lifecycle base class implementation for causal algorithms.
QueryLifecycleBeginBase(Inference, QueryOptions) - Constructor for class com.bayesserver.inference.QueryLifecycleBeginBase: For internal use.
QueryLifecycleEnd - Interface in com.bayesserver.inference: Contains information that is passed via the QueryLifecycle interface.
QueryLifecycleEndBase - Class in com.bayesserver.inference: Query end lifecycle base class implementation for causal algorithms.
QueryLifecycleEndBase(Inference, QueryOptions, QueryOutput) - Constructor for class com.bayesserver.inference.QueryLifecycleEndBase: For internal use.
QueryOptions - Interface in com.bayesserver.inference: Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryOutput - Interface in com.bayesserver.inference: Returns any information, in addition to the distributions, that is requested from a query.
QuerySamplingOptions - Interface in com.bayesserver.inference: Interface for approximate sampling inference algorithms, which can be implemented in addition to QueryOptions.

R

R_SQUARED - com.bayesserver.data.CrossValidationCombineMethod: Combines R-squared statistics calculated on different partitions of data.
R2CrossValidationTestResult - Class in com.bayesserver.data: Represents the R Squared statistic (Coefficient of determination) on a partition of data.
R2CrossValidationTestResult(double, double, double, double) - Constructor for class com.bayesserver.data.R2CrossValidationTestResult: Initializes a new instance of the R2CrossValidationTestResult class.
raisePropertyChanged(String) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
raisePropertyChanged(String) - Method in class com.bayesserver.optimization.GeneticOptionsBase
RandomDefault - Class in com.bayesserver: Default random number generator, that is consistent across the different APIs.
RandomDefault() - Constructor for class com.bayesserver.RandomDefault: Initializes a new instance of the RandomDefault class, using a seed generated from the system clock.
RandomDefault(int) - Constructor for class com.bayesserver.RandomDefault: Initializes a new instance of the RandomDefault class, with a specified seed.
randomize(RandomNumberGenerator) - Method in class com.bayesserver.Table: Randomizes the distribution such that each parent combination sums to 1.
RandomNumberGenerator - Interface in com.bayesserver: Interface for random number generation.
read() - Method in interface com.bayesserver.data.DataReader: Moves to the next record, if any exist.
read() - Method in class com.bayesserver.data.DataReaderFiltered: Moves to the next record, if any exist.
read() - Method in class com.bayesserver.data.DataTableReader
read() - Method in class com.bayesserver.data.DefaultDataReader: Reads the next (non temporal) record.
read() - Method in class com.bayesserver.data.timeseries.WindowDataReader: Moves to the next record, if any exist.
read(Evidence, ReadOptions) - Method in class com.bayesserver.data.DefaultEvidenceReader: Reads the next case (record).
read(Evidence, ReadOptions) - Method in interface com.bayesserver.data.EvidenceReader: Reads the next case (record).
read(String) - Method in interface com.bayesserver.NameValuesReader: Reads the value (as a stream) for a particular name.
ReaderOptions - Class in com.bayesserver.data: Options that apply to the reading of non temporal data.
ReaderOptions() - Constructor for class com.bayesserver.data.ReaderOptions: Initializes a new instance of the ReaderOptions class.
ReaderOptions(String) - Constructor for class com.bayesserver.data.ReaderOptions: Initializes a new instance of the ReaderOptions class.
ReaderOptions(String, String) - Constructor for class com.bayesserver.data.ReaderOptions: Initializes a new instance of the ReaderOptions class.
ReadInfo - Class in com.bayesserver.data: Provides information about a non temporal record.
ReadInfo(Object, double, DataRecord) - Constructor for class com.bayesserver.data.ReadInfo: Initializes a new instance of the ReadInfo class.
ReadInfo(Object, DataRecord) - Constructor for class com.bayesserver.data.ReadInfo: Initializes a new instance of the ReadInfo struct.
readNested(int) - Method in class com.bayesserver.data.DefaultDataReader: Reads the next record from a nested table.
ReadOptions - Interface in com.bayesserver.data: Provides information to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions).
readTemporal() - Method in class com.bayesserver.data.DefaultDataReader: Reads the next temporal record.
readTemporal(Evidence, ReadOptions) - Method in class com.bayesserver.data.DefaultEvidenceReader: Reads the next temporal record, setting evidence.
RegressionStatistics - Class in com.bayesserver.analysis: Calculates statistics for a network which is used to predict continuous values (regression).
RelevanceTreeInference - Class in com.bayesserver.inference: An exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.
RelevanceTreeInference(Network) - Constructor for class com.bayesserver.inference.RelevanceTreeInference: Initializes a new instance of the RelevanceTreeInference class, with the target Bayesian network.
RelevanceTreeInferenceFactory - Class in com.bayesserver.inference: Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
RelevanceTreeInferenceFactory() - Constructor for class com.bayesserver.inference.RelevanceTreeInferenceFactory
RelevanceTreeQueryLifecycleBegin - Class in com.bayesserver.inference: Query lifecycle begin implementation for the Relevance Tree algorithm.
RelevanceTreeQueryLifecycleEnd - Class in com.bayesserver.inference: Query end lifecycle implementation for the Relevance Tree algorithm.
RelevanceTreeQueryOptions - Class in com.bayesserver.inference: Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
RelevanceTreeQueryOptions() - Constructor for class com.bayesserver.inference.RelevanceTreeQueryOptions: Initializes a new instance of the RelevanceTreeQueryOptions class.
RelevanceTreeQueryOutput - Class in com.bayesserver.inference: Returns any information, in addition to the distributions, that is requested from a query.
RelevanceTreeQueryOutput() - Constructor for class com.bayesserver.inference.RelevanceTreeQueryOutput: Initializes a new instance of the RelevanceTreeQueryOutput class.
remove(int) - Method in class com.bayesserver.CustomPropertyCollection
remove(int) - Method in class com.bayesserver.data.DataColumnCollection: Removes the DataColumn at the given index.
remove(int) - Method in class com.bayesserver.data.DataRowCollection: Removes the row at the given index.
remove(int) - Method in class com.bayesserver.data.sampling.ExcludedVariables
remove(int) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
remove(int) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
remove(int) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
remove(int) - Method in class com.bayesserver.NetworkLinkCollection: Removes an element from the collection at the specified index.
remove(int) - Method in class com.bayesserver.NetworkNodeCollection: Removes an element from the collection at the specified index, and any links that it has.
remove(int) - Method in class com.bayesserver.NetworkNodeGroupCollection
remove(int) - Method in class com.bayesserver.NodeGroupCollection: Removes an element from the collection at the specified index.
remove(int) - Method in class com.bayesserver.NodeVariableCollection: Removes an element from the collection at the specified index.
remove(int) - Method in class com.bayesserver.StateCollection: Removes an element from the collection at the specified index.
remove(Link) - Method in class com.bayesserver.NetworkLinkCollection: Removes the Link from the collection.
remove(Node) - Method in class com.bayesserver.NetworkNodeCollection: Removes the Node from the collection.
remove(Variable) - Method in class com.bayesserver.NodeVariableCollection: Removes the Variable from the collection.
remove(String) - Method in class com.bayesserver.NodeGroupCollection: Removes the group from the collection.
REMOVE - com.bayesserver.CollectionAction: Specifies that an element was removed from the collection.
removeMonitor(NetworkMonitor) - Method in class com.bayesserver.Network: For internal use.
REPLACE - com.bayesserver.CollectionAction: Specifies that an element was replaced in the collection.
reset() - Method in class com.bayesserver.causal.CausalQueryOutputBase: Resets all values to their defaults.
reset() - Method in class com.bayesserver.CLGaussian: Resets all mean, covariance and weight entries to zero.
reset() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Resets all values to their defaults.
reset() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput: Resets all values to their defaults.
reset() - Method in interface com.bayesserver.inference.QueryOutput: Resets all values to their defaults.
reset() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput: Resets all values to their defaults.
reset() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput: Resets all values to their defaults.
reset() - Method in class com.bayesserver.TableIterator: Resets the iterator to the start.
RETAIN_QUERY_EVIDENCE - com.bayesserver.inference.QueryEvidenceMode: When predictions are made on a variable with evidence, the prediction simply returns the evidence.
RETRACT_QUERY_EVIDENCE - com.bayesserver.inference.QueryEvidenceMode: When predictions are made on a variable with evidence, the variable's own evidence is ignored.
reverse(Link) - Static method in class com.bayesserver.ArcReversal: Reverse the direction of a Link (known as arc reversal).
ROLLING - com.bayesserver.learning.parameters.TimeSeriesMode: All temporal distribution are updated at every time point.

S

SAMPLING - com.bayesserver.learning.parameters.InitializationMethod: Cases are selected at random to initialize distributions.
save(OutputStream) - Method in class com.bayesserver.inference.DefaultEvidence: Saves the evidence to the specified stream.
save(OutputStream) - Method in interface com.bayesserver.inference.Evidence: Saves the evidence to the specified stream.
save(OutputStream) - Method in class com.bayesserver.Network: Saves this Network to the specified output OutputStream.
save(String) - Method in class com.bayesserver.inference.DefaultEvidence: Saves the specified to the specified file.
save(String) - Method in interface com.bayesserver.inference.Evidence: Saves the specified to the specified file.
save(String) - Method in class com.bayesserver.Network: Saves this Network to the specified [path] overwriting the file if it already exists.
saveToString() - Method in class com.bayesserver.inference.DefaultEvidence: Saves evidence to a string, with UTF-8 encoding.
saveToString() - Method in interface com.bayesserver.inference.Evidence: Saves evidemce to a string, with UTF-8 encoding.
saveToString() - Method in class com.bayesserver.Network: Saves the network to a string, with UTF-8 encoding.
saveToString(String) - Method in class com.bayesserver.inference.DefaultEvidence: Saves evidence to a string, with the specified encoding.
saveToString(String) - Method in interface com.bayesserver.inference.Evidence: Saves evidence to a string, with the specified encoding.
saveToString(String) - Method in class com.bayesserver.Network: Saves the network to a string, with the specified encoding.
ScoreMethod - Enum in com.bayesserver.learning.structure: The scoring mechanism used to evaluate different Bayesian network structures during a search.
SearchLinkOutput - Class in com.bayesserver.learning.structure: Contains information about a new link learnt using the com.bayesserver.learning.structure.search.SearchStructuralLearning algorithm.
SearchStructuralLearning - Class in com.bayesserver.learning.structure: A structural learning algorithm for Bayesian networks based on Search and Score.
SearchStructuralLearning() - Constructor for class com.bayesserver.learning.structure.SearchStructuralLearning
SearchStructuralLearningOptions - Class in com.bayesserver.learning.structure: Options for structural learning with the com.bayesserver.learning.structure.search.SearchStructuralLearning class.
SearchStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.SearchStructuralLearningOptions
SearchStructuralLearningOutput - Class in com.bayesserver.learning.structure: Contains information returned from the com.bayesserver.learning.structure.search.SearchStructuralLearning algorithm.
SearchStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure: Progress information returned from the Search based structural learning algorithm.
SensitivityFunctionOneWay - Class in com.bayesserver.analysis: Represents the result on a one-way sensitivity to parameters analysis.
SensitivityFunctionTwoWay - Class in com.bayesserver.analysis: Represents the result on a two-way sensitivity to parameters analysis.
SensitivityToParameters - Class in com.bayesserver.analysis: Calculates the affect of one or more parameters on the value of a hypothesis.
SensitivityToParameters(Network, InferenceFactory) - Constructor for class com.bayesserver.analysis.SensitivityToParameters: Initializes a new instance of the SensitivityToParameters class .
SepsetDefinition - Class in com.bayesserver.inference: The definition of a sepset in a junction tree, without the instantiation of the distribution.
set(double, State...) - Method in class com.bayesserver.Table: Sets the table value corresponding to the given states.
set(double, StateContext...) - Method in class com.bayesserver.Table: Sets the table value corresponding to the given states and associated times.
set(int[], double) - Method in class com.bayesserver.TableAccessor: Sets the underlying Table value, using states corresponding to the order of variables in the TableAccessor.
set(int, double) - Method in class com.bayesserver.Table: Sets the Table value at the specified index into the 1-dimensional array.
set(int, double) - Method in class com.bayesserver.TableAccessor: Sets the underlying Table value, specified i.
set(int, CustomProperty) - Method in class com.bayesserver.CustomPropertyCollection
set(int, Distribution) - Method in class com.bayesserver.NodeDistributions: Sets a distribution at a particular temporal order.
set(int, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions: Sets a distribution expression at a particular temporal order.
set(int, QueryDistribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
set(int, QueryFunction) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
set(int, LinkConstraint) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
set(int, Link) - Method in class com.bayesserver.NetworkLinkCollection: Sets the Link object at the specified index.
set(int, Node) - Method in class com.bayesserver.NetworkNodeCollection: Sets the Node object at the specified index.
set(int, NodeGroup) - Method in class com.bayesserver.NetworkNodeGroupCollection
set(int, State) - Method in class com.bayesserver.StateCollection: Sets the State at the specified index.
set(int, Variable) - Method in class com.bayesserver.data.sampling.ExcludedVariables
set(int, Variable) - Method in class com.bayesserver.NetworkVariableCollection: Gets the Variable object at the specified index.
set(int, Variable) - Method in class com.bayesserver.NodeVariableCollection: Sets the Variable object at the specified index.
set(int, VariableContext) - Method in class com.bayesserver.VariableContextCollection: Gets the Variable object at the specified index.
set(int, Object) - Method in class com.bayesserver.data.DataRow: Sets the value at the specified index.
set(int, String) - Method in class com.bayesserver.NodeGroupCollection: Sets the group at the specified index.
set(NodeDistributionKey, Distribution) - Method in class com.bayesserver.NodeDistributions: Sets a distribution with particular properties, such as temporal order.
set(NodeDistributionKey, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions: Sets a distribution expression with particular properties, such as temporal order.
set(NodeDistributionKey, NodeDistributionKind, Distribution) - Method in class com.bayesserver.NodeDistributions: Sets a distribution with particular properties, such as temporal order.
set(NodeDistributionKey, NodeDistributionKind, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions: Sets a distribution expression with particular properties, such as temporal order.
set(NodeDistributionKey, NodeDistributionKind, ExpressionDistribution, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions: Sets a distribution expression with particular properties, such as temporal order.
set(NodeDistributionKind, Distribution) - Method in class com.bayesserver.NodeDistributions: Sets a particular kind of distribution on the node.
set(NodeDistributionKind, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions: Sets a particular kind of distribution expression on the node.
set(Node, Double) - Method in class com.bayesserver.inference.DefaultEvidence: Sets a node's variable to a particular value (hard evidence).
set(Node, Double) - Method in interface com.bayesserver.inference.Evidence: Sets a node's variable to a particular value (hard evidence).
set(Node, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence: Sets temporal evidence on a node with a single variable.
set(Node, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence: Sets temporal evidence on a node with a single variable.
set(Node, Double, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a node's single variable at a specified time.
set(Node, Double, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a node's single variable at a specified time.
set(Variable, Double) - Method in class com.bayesserver.inference.DefaultEvidence: Sets a variable to a particular value (hard evidence).
set(Variable, Double) - Method in interface com.bayesserver.inference.Evidence: Sets a variable to a particular value (hard evidence).
set(Variable, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence: Sets temporal evidence on a variable.
set(Variable, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence: Sets temporal evidence on a variable.
set(Variable, Double, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a variable at a specified time.
set(Variable, Double, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a variable at a specified time.
set(Variable, Double, Integer, InterventionType) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on the variable, in the form of an intervention (do-operator).
set(Variable, Double, Integer, InterventionType) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on the variable, in the form of an intervention (do-operator).
setAddNodeGroups(boolean) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets a value which determines whether network node groups are added for each group in a level.
setAdjustmentSet(AdjustmentSet) - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions: Sets the adjustment set, which must include all nodes that are causes of either treatments (X) or outcomes (Y) or both, except those with evidence set.
setAdjustmentSetOverride(AdjustmentSet) - Method in class com.bayesserver.causal.BackdoorQueryOptions: Gets an adjustment set to use during estimation, instead of the algorithm generating it automatically.
setAdjustmentSetXZOverride(AdjustmentSet) - Method in class com.bayesserver.causal.FrontDoorQueryOptions: Sets the 'adjustment set' for adjusting between treatments (X) and front-door nodes (Z).
setAdjustmentSetZYOverride(AdjustmentSet) - Method in class com.bayesserver.causal.FrontDoorQueryOptions: Sets the 'adjustment set' for adjusting between the front-door nodes (Z) and the outcomes (Y).
setAll(double) - Method in class com.bayesserver.Table: Sets all values in the Table to a specified value.
setAllowMissing(boolean) - Method in class com.bayesserver.optimization.DesignVariable: Determines whether the optimizer can consider missing values (evidence not set) on this variable.
setAllowNullDistributions(boolean) - Method in class com.bayesserver.ValidationOptions: Determines whether validation should succeed even if the required distribution(s) have not been assigned to a node.
setAllowNullFunctions(boolean) - Method in class com.bayesserver.ValidationOptions: Determines whether validation should succeed even if a function has not been assigned to a functiomn variable.
setAutoCommit(boolean) - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Sets the auto commit value to be set on each connection created.
setAutoDetectDiscreteLimit(int) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Sets the distinct value count, which when exceeded changes a variable from discrete to continuous.
setAutoReadTemporal(boolean) - Method in class com.bayesserver.data.DefaultEvidenceReader: Determines whether any temporal data is read automatically.
setBaseEvidence(Evidence) - Method in class com.bayesserver.causal.CausalInferenceBase: Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in interface com.bayesserver.inference.Inference: Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.LoopyBeliefInference: Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.RelevanceTreeInference: Optional evidence which can be used to calculate the lift of queries.
setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.VariableEliminationInference: Optional evidence which can be used to calculate the lift of queries.
setBounds(Bounds) - Method in class com.bayesserver.Node: Sets the size and location of the node.
setCalculateStatistics(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets a value indicating whether to calculate summary statistics in an extra iteration at the end of learning.
setCancel(boolean) - Method in interface com.bayesserver.Cancellation: When set to true attempts to cancel a long running operation.
setCancel(boolean) - Method in class com.bayesserver.DefaultCancellation: When set to true attempts to cancel a long running operation.
setCancellation(Cancellation) - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions: Gets of sets an instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets of sets an instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Allows cancellation of a query.
setCancellation(Cancellation) - Method in interface com.bayesserver.inference.QueryOptions: Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.TreeQueryOptions: Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Allows cancellation of a query.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets of sets the instance implementing Cancellation, used for cancellation.
setCancellation(Cancellation) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions: Used to cancel a long running operation.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.BackdoorCriterionOptions: The type of causal effect, such as Total or Direct.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.BackdoorValidationOptions
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions: The type of causal effect, such as Total or Direct.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.DisjunctiveCauseValidationOptions
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.FrontDoorCriterionOptions: The type of causal effect, such as Total or Direct.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.FrontDoorValidationOptions
setCausalEffectKind(CausalEffectKind) - Method in interface com.bayesserver.causal.IdentificationOptions: The type of causal effect, such as Total or Direct.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in interface com.bayesserver.inference.QueryOptions: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Sets the kind of effect to calculate.
setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Sets the kind of causal effect to optimize.
setCausalEffectKind(CausalEffectKind) - Method in interface com.bayesserver.optimization.OptimizerOptions: Sets the kind of causal effect to optimize.
setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalInferenceFactory(InferenceFactory) - Method in interface com.bayesserver.inference.QueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.TreeQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
setCausalObservability(CausalObservability) - Method in class com.bayesserver.Node: The CausalObservability of the node.
setCausesOfTreatmentsOrOutcomes(DisjunctiveCauseSet) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions: Sets a list of nodes which must include all causes of treatments (X) or causes of outcomes (Y) or causes of both.
setCausesOfTreatmentsOrOutcomes(DisjunctiveCauseSet) - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions: Sets the list of all nodes that are either causes of treatments (X) or outcomes (Y) or both.
setCleared(boolean) - Method in class com.bayesserver.data.DefaultReadOptions: Sets a value indicating whether the Evidence has been cleared prior to EvidenceReader.read(com.bayesserver.inference.Evidence, com.bayesserver.data.ReadOptions) being called.
setClusterVariableName(String) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the name of the cluster/latent node/variable created when more than 1 hidden state is detected.
setColumnName(String) - Method in class com.bayesserver.data.discovery.DiscretizationColumn: Sets the name of the column of data to be discretized.
setComparison(QueryComparison) - Method in class com.bayesserver.inference.QueryDistribution: Sets a value indicating whether queried values should be adjusted to show how they compare to the same query with no evidence, or base evidence.
setConflict(boolean) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in interface com.bayesserver.inference.QueryOptions: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(boolean) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Sets a value indicating whether the conflict measure should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setConflict(Double) - Method in class com.bayesserver.causal.CausalQueryOutputBase: Sets the conflict measure.
setConflict(Double) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Sets the conflict measure.
setConflict(Double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput: Sets the conflict measure.
setConflict(Double) - Method in interface com.bayesserver.inference.QueryOutput: Sets the conflict measure.
setConflict(Double) - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput: Sets the conflict measure.
setConflict(Double) - Method in class com.bayesserver.inference.VariableEliminationQueryOutput: Sets the conflict measure.
setContinuous(double) - Method in class com.bayesserver.learning.parameters.Priors: Sets the amount continuous distributions are adjusted during learning.
setContinuousTargetInterval(Interval<Double>) - Method in class com.bayesserver.analysis.AutoInsightOutput: Gets the target interval (if any).
setConvergenceMethod(ConvergenceMethod) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the method used to determine convergence of the learning algorithm.
setCovariance(int, int, int, double) - Method in class com.bayesserver.CLGaussian: Sets the covariance value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setCovariance(VariableContext, VariableContext, double) - Method in class com.bayesserver.CLGaussian: Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
setCovariance(VariableContext, VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(VariableContext, VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(VariableContext, VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Variable, double) - Method in class com.bayesserver.CLGaussian: Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
setCovariance(Variable, Variable, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double) - Method in class com.bayesserver.CLGaussian: Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
setCovariance(Variable, Integer, Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCovariance(Variable, Integer, Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
setCrossoverProbability(double) - Method in class com.bayesserver.optimization.GeneticOptionsBase: The probability of parents being crossed.
setDataColumn(String) - Method in class com.bayesserver.data.discovery.VariableDefinition: The name of the data column, containing the data used to generate the new variable.
setDataProgress(DataProgress) - Method in class com.bayesserver.data.DefaultEvidenceReader: Gets the instance used to report progress on the number of cases read.
setDataProgress(DataProgress) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Reports progress on the number of cases read.
setDataProgressInterval(int) - Method in class com.bayesserver.data.DefaultEvidenceReader: Sets a value which determines how often progress events are raised.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in interface com.bayesserver.inference.QueryOptions: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Sets the algorithm to use when a network contains Decision nodes.
setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.learning.parameters.OnlineLearningOptions: Sets the algorithm to use for adaption of decision graphs.
setDecisionPostProcessing(DecisionPostProcessingMethod) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the post processing method for decision nodes.
setDescription(String) - Method in class com.bayesserver.CustomProperty: An optional description for the custom property.
setDescription(String) - Method in class com.bayesserver.Link: Optional description for the link.
setDescription(String) - Method in class com.bayesserver.Network: An optional description for the Bayesian network.
setDescription(String) - Method in class com.bayesserver.Node: An optional description for the node.
setDescription(String) - Method in class com.bayesserver.NodeGroup: An optional description for the custom property.
setDescription(String) - Method in class com.bayesserver.State: Sets an optional description for the state.
setDescription(String) - Method in class com.bayesserver.Variable: An optional description for the variable.
setDetectIntegralFloats(boolean) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Sets a value, which when true tests floating point column data to see if the data is an integral type, which would then become a candidate to be a discrete variable when VariableValueType is not specified.
setDiscrete(double) - Method in class com.bayesserver.learning.parameters.Priors: Sets the amount distributions containing discrete variables are adjusted during learning.
setDiscretePriorMethod(DiscretePriorMethod) - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Sets the type of discrete prior to use for this distribution.
setDiscretePriorMethod(DiscretePriorMethod) - Method in class com.bayesserver.learning.parameters.Priors: The default discrete prior to use for discrete distributions during parameter learning.
setDiscretizationMethod(DiscretizationMethod) - Method in class com.bayesserver.data.discovery.VariableDefinition: Sets the method (algorithm) to use for discretization, if any.
setDistance(Double) - Method in class com.bayesserver.inference.QueryDistribution: The distance between this query calculated with base evidence or no evidence, and when calculated with evidence.
setDistribution(Distribution) - Method in class com.bayesserver.Node: Returns the distribution currently associated with the Node.
setEmptyStringAction(EmptyStringAction) - Method in class com.bayesserver.data.discovery.VariableDefinition: Determines the action to take if an empty string is encountered.
setEnsureTestWithoutCluster(boolean) - Method in class com.bayesserver.analysis.ClusterCountOptions: Sets a value which indicates whether a test must be included which excludes the cluster variable altogether.
setEnumerateAllMissing(boolean) - Method in class com.bayesserver.analysis.CombinationOptions: Sets a value which indicates whether the combination where all states are null/missing should be included in the enumeration.
setEnumerateMissing(boolean) - Method in class com.bayesserver.analysis.CombinationOptions: Sets a value which indicates whether null/missing values should be enumerated in addition to each state.
setEvidence(Evidence) - Method in class com.bayesserver.causal.CausalInferenceBase: Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in interface com.bayesserver.inference.Inference: Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.LoopyBeliefInference: Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.RelevanceTreeInference: Represents the evidence, or case data (e.g.
setEvidence(Evidence) - Method in class com.bayesserver.inference.VariableEliminationInference: Sets the evidence (case data, e.g.
setEvidenceKind(DesignEvidenceKind) - Method in class com.bayesserver.optimization.DesignVariable: Determines whether the optimizer uses hard or soft/virtual evidence for this variable.
setEvidenceToSimplify(Evidence) - Method in class com.bayesserver.optimization.GeneticSimplificationOptions: The evidence from a previous optimization.
setEvidenceType(EvidenceType) - Method in class com.bayesserver.inference.EvidenceTypes: Sets the EvidenceType.
setExcludeNullDistributions(boolean) - Method in class com.bayesserver.ParameterCountOptions: Sets a value indicating whether null distributions are excluded from the parameter count.
setExpressionAlias(String) - Method in class com.bayesserver.Variable: Sets a c-style name for a variable that can be used as an alias in expressions.
setFactory(InferenceFactory) - Method in class com.bayesserver.analysis.ImpactOptions: Sets the inference factory which is used to create inference engines during an impact analysis.
setFactory(InferenceFactory) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Sets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
setFetchSize(int) - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Sets the fetch size to be set on each statement created.
setFixedData(Evidence) - Method in class com.bayesserver.data.sampling.DataSampler: Sets any evidence that should be fixed for each sample.
setFrontDoorNodesOverride(FrontDoorSet) - Method in class com.bayesserver.causal.FrontDoorQueryOptions: Sets the set of front-door nodes (Z) used by the front-door adjustment.
setFunction(QueryExpression) - Method in class com.bayesserver.Variable: Sets an expression, which is evaluated during a query, and can be based on other queries and expressions.
setGap(double) - Method in class com.bayesserver.DecomposeOptions: The gap between decomposed nodes, used when laying out new nodes.
setHasZeroIntercepts(boolean) - Method in class com.bayesserver.NodeDistributionOptions: Determines whether CLGaussian intercept terms are fixed to zero.
setIncludeGlobalCovariance(boolean) - Method in class com.bayesserver.learning.parameters.Priors: When Gaussian distributions are adjusted according to the Priors.getContinuous() prior, this property determines whether the global covariance should be included in the adjustment, as well as the global variance.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Determines when an InconsistentEvidenceException is raised.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Determines when an InconsistentEvidenceException is raised.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Determines when an InconsistentEvidenceException is raised.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in interface com.bayesserver.inference.QueryOptions: Determines when an InconsistentEvidenceException is raised.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Determines when an InconsistentEvidenceException is raised.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.TreeQueryOptions: Determines when an InconsistentEvidenceException is raised.
setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Determines when an InconsistentEvidenceException is raised.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.AssociationOptions: Sets the inference factory used for link strength calculations.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.AutoInsightOptions: Sets the inference factory used for link strength calculations.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.ClusterCountOptions: Sets the factory which is used to create inference engines during the cluster count tests.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions: Sets the factory which is used to create inference engines during the in-sample anomaly detection process.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.causal.AbductionOptions: Used to create an inference engine, to determine the values for the characterstic variables.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the inference factory used during scoring.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the inference factory used during scoring.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets the inference factory used during scoring.
setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Used to create one or more inference engines, used by the algorithm to determine the fitness of possible solutions.
setInferenceFactory(InferenceFactory) - Method in interface com.bayesserver.optimization.OptimizerOptions: Creates one or more inference engines used by the optimization algorithm.
setInfiniteExtremes(boolean) - Method in class com.bayesserver.data.discovery.DiscretizationOptions: Sets a value indicating whether the first and last intervals extend to negative and positive infinity respectively.
setInitialize(Boolean) - Method in class com.bayesserver.learning.parameters.DistributionSpecification: Sets a flag indicating whether the distribution should be initialized.
setInitializeDistributions(boolean) - Method in class com.bayesserver.learning.parameters.InitializationOptions: Indicates whether or not to initialize distributions by default.
setInterventionType(InterventionType) - Method in class com.bayesserver.inference.EvidenceTypes: Sets the InterventionType.
setInterventionType(InterventionType) - Method in class com.bayesserver.optimization.DesignVariable: Determines the evidence intervention type for this variable.
setIsApproximate(boolean) - Method in class com.bayesserver.analysis.AutoInsightOutput: Gets a value which when true indicates that the auto-insight calculations were approximated using sampling.
setIsEnabled(boolean) - Method in class com.bayesserver.inference.QueryDistribution: Sets a value indicating whether the distribution should be queried.
setIsEnabled(boolean) - Method in class com.bayesserver.inference.QueryFunction: Sets a value indicating whether the function should be evaluated.
setIsImpliedEvidenceEnabled(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets a value indicating whether to detect implied evidence during the calculation.
setIsInternal(boolean) - Method in class com.bayesserver.Network: For internal use only.
setIsProper(boolean) - Method in class com.bayesserver.causal.BackdoorGraphOptions: Sets a value which determines whether a 'proper Backdoor graph' is constructed.
setJSDivergence(AutoInsightJSDivergence) - Method in class com.bayesserver.analysis.AutoInsightOptions: Sets a value which determines the type of Jensen Shannon divergence calculations to perform, if any.
setJunctionTree(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets a value indicating whether or not to generate a junction tree definition.
setKeepEvidenceNotAnalyzed(boolean) - Method in class com.bayesserver.analysis.ImpactOptions: Sets a value which when true retains evidence not being analysed, or when false ignores it.
setKeepEvidenceNotAnalyzed(boolean) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Sets a value which when true retains evidence not being analysed, or when false ignores it.
setKind(VariableKind) - Method in class com.bayesserver.data.discovery.VariableDefinition: Sets the VariableKind for the new variable.
setKLDivergence(AutoInsightKLDivergence) - Method in class com.bayesserver.analysis.AutoInsightOptions: Sets a value which determines the type of KL divergence calculations to perform, if any.
setLocked(boolean) - Method in class com.bayesserver.CLGaussian: Locks or unlocks a distribution.
setLocked(boolean) - Method in interface com.bayesserver.Distribution: Locks or unlocks a distribution.
setLocked(boolean) - Method in class com.bayesserver.Table: Locks or unlocks a distribution.
setLogarithmBase(LogarithmBase) - Method in class com.bayesserver.analysis.ValueOfInformationOptions: The logarithm base to use when calculating ValueOfInformation.
setLogLikelihood(boolean) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in interface com.bayesserver.inference.QueryOptions: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(boolean) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Sets a value indicating whether the log-likelihood of a case should be calculated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setLogLikelihood(Double) - Method in class com.bayesserver.causal.CausalQueryOutputBase: Sets the log-likelihood value.
setLogLikelihood(Double) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Sets the log-likelihood value.
setLogLikelihood(Double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput: Sets the log-likelihood value.
setLogLikelihood(Double) - Method in class com.bayesserver.inference.QueryDistribution: The log-likelihood specific to the evidence used to calculate this query.
setLogLikelihood(Double) - Method in interface com.bayesserver.inference.QueryOutput: Sets the log-likelihood value.
setLogLikelihood(Double) - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput: Sets the log-likelihood value.
setLogLikelihood(Double) - Method in class com.bayesserver.inference.VariableEliminationQueryOutput: Sets the log-likelihood value.
setLogWeight(double) - Method in class com.bayesserver.inference.DefaultEvidence: Sets the natural logarithm of Evidence.getWeight().
setLogWeight(double) - Method in interface com.bayesserver.inference.Evidence: Sets the natural logarithm of Evidence.getWeight().
setLowerBound(Double) - Method in class com.bayesserver.optimization.DesignState: The minimum value allowed for this variable/state during the optimization process.
setMaxEvidenceSubsetSize(int) - Method in class com.bayesserver.analysis.ImpactOptions: Sets the maximum size of evidence subsets to consider.
setMaxEvidenceSubsetSize(int) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Sets the maximum size of evidence subsets to consider.
setMaximum(T) - Method in class com.bayesserver.Interval: Sets the maximum interval value.
setMaximumAdjustmentSets(Integer) - Method in class com.bayesserver.causal.BackdoorCriterionOptions: Limits the number of adjustment sets generated.
setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
setMaximumClusterCount(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the maximum number of clusters generated.
setMaximumClustersPerGroup(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the maximum number of clusters generated for each group.
setMaximumConcurrency(Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the maximum number of inference engines used during learning.
setMaximumConcurrency(Integer) - Method in class com.bayesserver.optimization.GeneticOptionsBase
setMaximumConditional(int) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Sets the maximum number of conditional variables to consider during independence testing.
setMaximumEndPoint(IntervalEndPoint) - Method in class com.bayesserver.Interval: Sets the end point type for the maximum value of the interval.
setMaximumGroupsPerLevel(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the maximum number of groups created per level.
setMaximumIterations(int) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the maximum number of iterations that parameter learning will perform.
setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the maximum number of iterations used by parameter learning to score each configuration.
setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the maximum number of iterations used by parameter learning to score each configuration.
setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets the optional maximum number of iterations (moves) made during the search procedure.
setMaximumLevels(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the maximum number of levels generated by the algorithm.
setMaximumSupport(int) - Method in class com.bayesserver.learning.parameters.InitializationOptions: Limits the amount of support each distribution is given during initialization.
setMaximumTemporalOrder(int) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Sets the maximum order of temporal links that are considered during learning.
setMean(int, int, double) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setMean(VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
setMean(Variable, double) - Method in class com.bayesserver.CLGaussian: Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setMean(Variable, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
setMean(Variable, Integer, double) - Method in class com.bayesserver.CLGaussian: Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setMean(Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
setMean(Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
setMethod(BackdoorMethod) - Method in class com.bayesserver.causal.BackdoorCriterionOptions
setMethod(InitializationMethod) - Method in class com.bayesserver.learning.parameters.InitializationOptions: Determines the algorithm used for initialization.
setMinimum(T) - Method in class com.bayesserver.Interval: Sets the minimum interval value.
setMinimumEndPoint(IntervalEndPoint) - Method in class com.bayesserver.Interval: Sets the end point type for the minimum value of the interval.
setMissingDataProbability(double) - Method in class com.bayesserver.data.sampling.DataSamplingOptions: When positive, sets a certain percentage of values to missing (except when DataSamplingOptions.getMissingDataProbabilityMin() has a value).
setMissingDataProbabilityMin(Double) - Method in class com.bayesserver.data.sampling.DataSamplingOptions: When set, the missing data probability for each case varies randomly between DataSamplingOptions.getMissingDataProbabilityMin() and DataSamplingOptions.getMissingDataProbability().
setMonitorLogLikelihood(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Calculates the log likelihood at each iteration.
setMutationProbability(double) - Method in class com.bayesserver.optimization.GeneticOptionsBase: The probability of genes being mutated.
setMutualInformation(boolean) - Method in class com.bayesserver.learning.structure.FeatureSelectionOptions: Sets a value which when true calculates the mutual information between each target and test.
setName(String) - Method in class com.bayesserver.data.discovery.VariableDefinition: Sets the name for the new variable.
setName(String) - Method in class com.bayesserver.Network: An optional name for the Bayesian network.
setName(String) - Method in class com.bayesserver.Node: The name of the node.
setName(String) - Method in class com.bayesserver.NodeGroup: Gets the name, which must be unique per NetworkNodeGroupCollection.
setName(String) - Method in class com.bayesserver.State: Sets the name of the state.
setName(String) - Method in class com.bayesserver.Variable: Sets the name of the variable.
setNetwork(Network) - Method in class com.bayesserver.causal.CausalInferenceBase
setNetwork(Network) - Method in class com.bayesserver.data.DefaultCrossValidationNetwork: Sets the network learnt from a cross validation partitioning.
setNodeWidthOverride(Double) - Method in class com.bayesserver.DecomposeOptions: Sets a value that can be used to override the width of nodes, used when laying out new nodes.
setNodeWidthOverride(Double) - Method in class com.bayesserver.UnrollOptions: Sets a value that can be used to override the width of nodes, used when laying out nodes.
setNoisyOrder(NoisyOrder) - Method in class com.bayesserver.Link: Sets a value which determines the nature of the effect between the parent node (from) and a noisy child node (to).
setNoisyType(NoisyType) - Method in class com.bayesserver.NodeDistributionOptions: Sets a value which identifies this node as a noisy node or not.
setNormalization(TableExpressionNormalization) - Method in class com.bayesserver.TableExpression: Gets of sets the normalization method, if any, to use once the Table values have been generated, but before assignment to a node.
setOnExecuteReader(ExecuteEvidenceReader) - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand: Sets a function that is called when a new reader is created.
setPartitionCount(int) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets the number of partitions used by scoring functions that use cross validation.
setPartitions(int) - Method in class com.bayesserver.analysis.ClusterCountOptions: Sets the number of cross validation partitions to use.
setPartitions(int) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions: Sets the number of cross validation partitions to use.
setPartitions(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the number of cross validation partitions to use when scoring each cluster count.
setPartitions(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the number of cross validation partitions to use when scoring each cluster count.
setPopulationSize(int) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Sets the number of chromosomes in each generation.
setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.Clustering: Gets an instance that receive progress notifications.
setProgress(DiscretizeProgress) - Method in interface com.bayesserver.data.discovery.Discretize: Gets an instance that receive progress notifications.
setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.EqualFrequencies: Gets an instance that receive progress notifications.
setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.EqualIntervals: Gets an instance that receive progress notifications.
setProgress(VariableGeneratorProgress) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Gets of sets the instance implementing VariableGeneratorProgress, used for progress notifications.
setProgress(ParameterLearningProgress) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Gets of sets the instance implementing ParameterLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Gets of sets the instance implementing StructuralLearningProgress, used for progress notifications.
setProgress(OptimizerProgress) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Gets of sets the instance implementing OptimizerProgress, used for progress notifications.
setProgress(OptimizerProgress) - Method in interface com.bayesserver.optimization.OptimizerOptions: Gets of sets the instance implementing OptimizerProgress, used for progress notifications.
setPropagation(PropagationMethod) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in interface com.bayesserver.inference.QueryOptions: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Sets the propagation method to be used during inference.
setPropagation(PropagationMethod) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions: Sets the propagation method to use during marginalization.
setQueryDistance(QueryDistance) - Method in class com.bayesserver.inference.QueryDistribution: Sets a value indicating whether the distance should be calculated between the query calculated with base evidence (or no evidence), and the same query calculated with evidence.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.causal.CausalInferenceBase: Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in interface com.bayesserver.inference.Inference: Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.LoopyBeliefInference: Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.RelevanceTreeInference: Sets the collection of distributions to calculate.
setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.VariableEliminationInference: The collection of distributions required from a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in interface com.bayesserver.inference.QueryOptions: Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.TreeQueryOptions: Determines whether evidence is retracted for each query.
setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Determines whether evidence is retracted for each query.
setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.causal.CausalInferenceBase: Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
setQueryFunctions(QueryFunctionCollection) - Method in interface com.bayesserver.inference.Inference: Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.LoopyBeliefInference: Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.RelevanceTreeInference: Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.VariableEliminationInference: Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.causal.CausalInferenceBase: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory: Sets a query lifecycle instance.
setQueryLifecycle(QueryLifecycle) - Method in interface com.bayesserver.inference.Inference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.LikelihoodSamplingInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.LoopyBeliefInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.RelevanceTreeInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.VariableEliminationInference: Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
setQueryLogLikelihood(boolean) - Method in class com.bayesserver.inference.QueryDistribution: Determines whether or not to calculate the QueryDistribution.getLogLikelihood() specific to the evidence used to calculate this query.
setQueryLogLikelihood(Boolean) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
setQueryLogLikelihood(Boolean) - Method in interface com.bayesserver.optimization.OptimizerOptions: Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
setQueryTimeout(int) - Method in class com.bayesserver.data.DatabaseDataReaderCommand: Sets the timeout to be used when statements are executed.
setRemoveAbductionEvidence(boolean) - Method in class com.bayesserver.causal.AbductionOptions: Sets a value which when true removes the abduction evidence, after updating the characteristic variables.
setReturnType(ExpressionReturnType) - Method in class com.bayesserver.FunctionVariableExpression
setReturnType(ExpressionReturnType) - Method in class com.bayesserver.TableExpression
setRoot(Node) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Sets the root of the Chow-Liu tree.
setRoot(Node) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Sets the root of the TAN tree.
setRunsPerConfiguration(int) - Method in class com.bayesserver.analysis.ClusterCountOptions: Gets of sets the number of times training is re-run for each network structure tested.
setRunsPerConfiguration(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the number of times training is re-run for each network structure tested.
setRunsPerConfiguration(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the number of times training is re-run for each network structure tested.
setSampleCount(Integer) - Method in class com.bayesserver.analysis.AutoInsightSamplingOptions: The number of samples used to approximate sufficient statistics, when exact inference is not possible.
setSampleCount(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets a value indicating how many samples cases to generate in order to approximate the current query.
setSampleCount(Integer) - Method in interface com.bayesserver.inference.QuerySamplingOptions: Sets a value indicating how many samples cases to generate in order to approximate the current query.
setSamplingProbability(double) - Method in class com.bayesserver.learning.parameters.InitializationOptions: A value between 0 and 1 (inclusive) indicating what probability of cases to use for initialization.
setSaveHyperparameters(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets a value indicating whether hyperparameters (e.g.
setScoreMethod(ScoreMethod) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets the scoring method used to evaluate search moves.
setSeed(int) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput: Sets the seed used by the random number generator.
setSeed(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets an optional seed for the random number generator.
setSeed(Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the seed used to generate random numbers for initialization.
setSeed(Integer) - Method in class com.bayesserver.optimization.GeneticOptionsBase: The seed for the random number generator used by the Genetic Algorithm.
setSequenceLength(Integer) - Method in class com.bayesserver.data.sampling.DataSamplingOptions: The sequence length generated for each sample from networks with temporal nodes.
setShift(int) - Method in class com.bayesserver.data.timeseries.WindowOptions: Sets the number of records between successive windows.
setSignificanceLevel(double) - Method in class com.bayesserver.learning.structure.IndependenceOptions: Sets the significance level used to accept or reject (conditional) independence tests.
setSimpleVariance(double) - Method in class com.bayesserver.learning.parameters.Priors: Used to make a fixed adjustment to all covariance matrices during learning, by increasing each diagonal (variance) entry.
setSimplifyTolerance(double) - Method in class com.bayesserver.optimization.GeneticSimplificationOptions: This is a non negative number which determines whether a simplified solution is close enough to the best found.
setSliceGap(double) - Method in class com.bayesserver.UnrollOptions: Sets the gap between time slices.
setSortOrder(SortOrder) - Method in class com.bayesserver.data.discovery.VariableDefinition: Sets the sort order for states of a new discrete variable.
setStagnationCount(Integer) - Method in class com.bayesserver.optimization.GeneticTerminationOptions: Sets the number of generations with equal objective values that are evaluated before the optimizer terminates.
setState(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
setState(Node, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
setState(Node, Integer, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
setState(Node, Integer, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
setState(State) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a discrete state (hard evidence).
setState(State) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a discrete state (hard evidence).
setState(State, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
setState(State, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
setState(State, Integer, InterventionType) - Method in class com.bayesserver.inference.DefaultEvidence: Sets evidence on a discrete state (hard evidence), in the form of an intervention (do-operator).
setState(State, Integer, InterventionType) - Method in interface com.bayesserver.inference.Evidence: Sets evidence on a discrete state (hard evidence), in the form of an intervention (do-operator).
setState(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets a discrete variable to a particular state (hard evidence).
setState(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets a discrete variable to a particular state (hard evidence).
setState(Variable, Integer, Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
setState(Variable, Integer, Integer) - Method in interface com.bayesserver.inference.Evidence: Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
setStates(Node, double[]) - Method in class com.bayesserver.inference.DefaultEvidence: Sets soft evidence for a discrete node with a single variable.
setStates(Node, double[]) - Method in interface com.bayesserver.inference.Evidence: Sets soft evidence for a discrete node with a single variable.
setStates(Node, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets soft evidence for a discrete node with a single variable, at a specified time.
setStates(Node, double[], Integer) - Method in interface com.bayesserver.inference.Evidence: Sets soft evidence for a discrete node with a single variable, at a specified time.
setStates(Variable, double[]) - Method in class com.bayesserver.inference.DefaultEvidence: Sets soft evidence for a particular discrete variable.
setStates(Variable, double[]) - Method in interface com.bayesserver.inference.Evidence: Sets soft evidence for a particular discrete variable.
setStates(Variable, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence: Sets soft evidence for a particular discrete variable at a specified time.
setStates(Variable, double[], Integer) - Method in interface com.bayesserver.inference.Evidence: Sets soft evidence for a particular discrete variable at a specified time.
setStateValueType(StateValueType) - Method in class com.bayesserver.data.discovery.VariableDefinition: Sets the StateValueType for the new variable.
setStateValueType(StateValueType) - Method in class com.bayesserver.Variable: Sets the type of value that states belonging to this variable can represent.
setStop(boolean) - Method in interface com.bayesserver.Stop: When true, indicates to the algorithm to complete early.
setStopping(Stop) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in class com.bayesserver.optimization.GeneticOptionsBase: Sets the instance implementing Stop used for early stopping.
setStopping(Stop) - Method in interface com.bayesserver.optimization.OptimizerOptions: Sets the instance implementing Stop used for early stopping.
setSubsetMethod(ImpactSubsetMethod) - Method in class com.bayesserver.analysis.ImpactOptions: Sets a value which determines whether evidence subsets are included, excluded or both.
setSubsetMethod(LogLikelihoodAnalysisSubsetMethod) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions: Sets a value which determines whether evidence subsets are included, excluded or both.
setSuggestedBinCount(int) - Method in class com.bayesserver.analysis.HistogramDensityOptions: Sets the approximate number of bins to use to represent the approximate density function.
setSuggestedBinCount(int) - Method in class com.bayesserver.data.discovery.DiscretizationOptions: Sets the number of suggested bins to use during discretization.
setSyncNodeVariableName(boolean) - Static method in class com.bayesserver.Network: When true synchronizes Variable names with their containing Node.
setTarget(Node) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions: Sets the target of the TAN tree.
setTemporalType(TemporalType) - Method in class com.bayesserver.Node: The TemporalType of the node.
setTerminalTime(Integer) - Method in class com.bayesserver.analysis.DSeparationOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.analysis.ValueOfInformationOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.causal.CausalQueryOptionsBase: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in interface com.bayesserver.inference.QueryOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTerminalTime(Integer) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions: Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
setTestIndependence(boolean) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets a value which when true uses independence tests to reduce the search space.
setTestSingleCluster(boolean) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions: Sets a value which determines whether a test is performed for a single cluster (i.e.
setText(String) - Method in interface com.bayesserver.Expression: Sets the expression text.
setText(String) - Method in class com.bayesserver.FunctionVariableExpression: Sets the expression text.
setText(String) - Method in class com.bayesserver.TableExpression: Sets the expression text, which is run for each cell in the table.
setTimes(int[]) - Method in class com.bayesserver.data.timeseries.WindowOptions: Sets the times to include in the window.
setTimeSeriesMode(TimeSeriesMode) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the mode in which time series distributions are learned.
setTolerance(double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions: Sets the tolerance used to determine whether or not the approximate inference process has converged.
setTolerance(Double) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions: Sets the tolerance used to determine whether or not parameter learning has converged.
setTolerance(Double) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions: Sets the tolerance used to determine whether or not a search move is a significant improvement.
setTreatmentValues(List<Double>) - Method in class com.bayesserver.causal.EffectsAnalysisOptions: A list of treatment values to evaluate the causal effect on the outcome for.
setTreeWidth(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions: Sets a value indicating whether or not to calculate the tree width.
setUnweightedCaseCount(long) - Method in class com.bayesserver.data.DataProgressEventArgs: Gets the number of cases read so far.
setUnweightedTemporalCount(Long) - Method in class com.bayesserver.data.DataProgressEventArgs: Gets the number of temporal rows read so far for all cases.
setUpperBound(Double) - Method in class com.bayesserver.optimization.DesignState: The maximum value allowed for this variable/state during the optimization process.
setValue(double) - Method in class com.bayesserver.TableIterator: Sets the underlying Table value at the current position of the iterator.
setValue(Double) - Method in class com.bayesserver.data.discovery.WeightedValue: Sets the value, which can be null.
setValue(Object) - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
setValue(Object) - Method in class com.bayesserver.inference.QueryFunctionOutput: Holds the result of a function evaluation at query time.
setValue(Object) - Method in class com.bayesserver.State: Sets an optional value for a state, such as an interval for discretized variables.
setValue(String) - Method in class com.bayesserver.CustomProperty: The custom property value.
setValueType(VariableValueType) - Method in class com.bayesserver.data.discovery.VariableDefinition: Sets the VariableValueType for the new variable.
setVariance(int, int, double) - Method in class com.bayesserver.CLGaussian: Sets the variance value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setVariance(VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, double) - Method in class com.bayesserver.CLGaussian: Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setVariance(Variable, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, Integer, double) - Method in class com.bayesserver.CLGaussian: Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
setVariance(Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setVariance(Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
setWeight(double) - Method in class com.bayesserver.data.discovery.WeightedValue: Sets the weight (support) for the WeightedValue.getValue().
setWeight(double) - Method in class com.bayesserver.inference.DefaultEvidence: Sets a weight that can be applied to the evidence.
setWeight(double) - Method in interface com.bayesserver.inference.Evidence: Sets a weight that can be applied to the evidence.
setWeight(int, int, int, double) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
setWeight(VariableContext, VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(VariableContext, VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(VariableContext, VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Variable, double) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
setWeight(Variable, Variable, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Integer, Variable, Integer, double) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
setWeight(Variable, Integer, Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Integer, Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeight(Variable, Integer, Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian: Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
setWeightColumn(String) - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions: Sets a column that contains case weights for each record.
setWeightColumn(String) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions: Sets the name of a column which contains a weight (support) for each case.
setWeightedCaseCount(double) - Method in class com.bayesserver.data.DataProgressEventArgs: Gets the number of cases read so far.
setWeightedCaseCount(double) - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
setWindowColumnName(String) - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions: Sets the name of the column which will contain the window identifier.
setWindowTimeColumnName(String) - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions: Sets the name of the column which will contain the window time.
SIMPLE - com.bayesserver.analysis.AutoInsightJSDivergence: When distributions are compared during the auto-insight process, marginal distributions, rather than joint distributions, are compared.
SIMPLE - com.bayesserver.analysis.AutoInsightKLDivergence: When distributions are compared during the auto-insight process, marginal distributions, rather than joint distributions, are compared.
SINGLE_POLICY_UPDATING_FULL - com.bayesserver.inference.DecisionAlgorithm: A Single Policy Updating (SPU) algorithm, that can compute a wider range of queries than the 'light' version, but is less efficient that the light version, both in terms of memory and speed.
SINGLE_POLICY_UPDATING_LIGHT - com.bayesserver.inference.DecisionAlgorithm: A Single Policy Updating (SPU) algorithm, that is more efficient than the full version, as it treats the leaf MEU inputs as independent.
size() - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
size() - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
size() - Method in class com.bayesserver.analysis.ConfusionMatrixCell: Gets the count (support) for this cell.
size() - Method in class com.bayesserver.analysis.DSeparationTestResultCollection
size() - Method in class com.bayesserver.analysis.HistogramDensityItem: The number of data rows that fell into this interval.
size() - Method in class com.bayesserver.CustomPropertyCollection
size() - Method in class com.bayesserver.data.DataColumnCollection: Gets the number of columns in the collection.
size() - Method in class com.bayesserver.data.DataRowCollection: Gets the number of rows in the collection.
size() - Method in class com.bayesserver.data.sampling.ExcludedVariables
size() - Method in class com.bayesserver.inference.DefaultEvidence: Gets the count of variables with either hard, soft or temporal evidence set.
size() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
size() - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
size() - Method in class com.bayesserver.inference.EliminationDefinitionCollection
size() - Method in interface com.bayesserver.inference.Evidence: Gets the count of variables with either hard, soft or temporal evidence set.
size() - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
size() - Method in class com.bayesserver.NetworkLinkCollection
size() - Method in class com.bayesserver.NetworkNodeCollection
size() - Method in class com.bayesserver.NetworkNodeGroupCollection
size() - Method in class com.bayesserver.NetworkVariableCollection: Gets the number of elements contained in the NetworkVariableCollection instance.
size() - Method in class com.bayesserver.NodeDistributionExpressions: Gets the number of distributions in the container.
size() - Method in class com.bayesserver.NodeDistributions: Gets the number of distributions in the container.
size() - Method in class com.bayesserver.NodeGroupCollection: Gets the number of elements contained in the NodeGroupCollection instance.
size() - Method in class com.bayesserver.NodeLinkCollection
size() - Method in class com.bayesserver.NodeVariableCollection: Gets the number of elements contained in the NodeVariableCollection instance.
size() - Method in class com.bayesserver.StateCollection
size() - Method in class com.bayesserver.Table: The data count in the Table.
size() - Method in class com.bayesserver.TableAccessor: Gets the count of values in the underlying Table.
size() - Method in class com.bayesserver.TableIterator: Gets the count of values in the underlying Table.
size() - Method in class com.bayesserver.VariableContextCollection: Gets the number of elements contained in the collection.
SOFT - com.bayesserver.inference.EvidenceType: A distribution is used to indicate evidence that is uncertain.
SOFT - com.bayesserver.optimization.DesignEvidenceKind: Soft/virtual evidence can be set on a discrete variable.
SoftEvidence - Class in com.bayesserver.inference: Helper methods for manipulating soft/virtual evidence.
sort(Network) - Static method in class com.bayesserver.TopologicalSort: Returns the nodes in a Bayesian network sorted in topological order.
SortOrder - Enum in com.bayesserver.data.discovery: The sort order of states for new discrete variables.
sortWithDepth(Network) - Static method in class com.bayesserver.TopologicalSort: Returns the nodes in a Bayesian network sorted and grouped in topological order.
State - Class in com.bayesserver: Represents a state of a variable.
State() - Constructor for class com.bayesserver.State: Initializes a new instance of the State class.
State(String) - Constructor for class com.bayesserver.State: Initializes a new instance of the State class with the specified [name].
State(String, Object) - Constructor for class com.bayesserver.State: Initializes a new instance of the State class with the specified [name] and [value].
StateCollection - Class in com.bayesserver: Represents a collection of states belonging to a Variable.
StateContext - Class in com.bayesserver: Identifies a State and contextual information such as the time (zero based).
StateContext(State, Integer) - Constructor for class com.bayesserver.StateContext: Initializes a new instance of StateContext.
stateCount(int) - Method in class com.bayesserver.Table: Gets the number of states of a variable at the time this instance was constructed.
StateNotFoundAction - Enum in com.bayesserver.data: Determines the action to take when a state name or value cannot be matched to a variable state.
stateRepeat(int) - Method in class com.bayesserver.Table: Gets the number of times each state is repeated for a Variable in the Table layout.
statesCollectionChange(Variable, int, State, State, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
StateValueType - Enum in com.bayesserver: The type of value represented by a State.
Stop - Interface in com.bayesserver: Interface to allow early completion of a long running task.
STRING - com.bayesserver.ExpressionReturnType: Expression returns a string.
StructuralLearning - Interface in com.bayesserver.learning.structure: Defines methods for learning the structure (links) of a Bayesian network.
StructuralLearningOptions - Interface in com.bayesserver.learning.structure: Options governing a structural learning algorithm.
StructuralLearningOutput - Interface in com.bayesserver.learning.structure: Contains information returned from a structural learning algorithm.
StructuralLearningProgress - Interface in com.bayesserver.learning.structure: Interface to provide progress information during structural learning.
StructuralLearningProgressInfo - Interface in com.bayesserver.learning.structure: Interface to provide progress information during structural learning.
sum() - Method in class com.bayesserver.Table: Calculates the sum of all values in the Table.
SUM - com.bayesserver.PropagationMethod: Sum propagation is the default method, used to perform standard probabilistic inference.

T

Table - Class in com.bayesserver: Used to represent probability distributions, conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.
Table(Node) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with the specified node variables.
Table(Node...) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with all the variables from the supplied nodes.
Table(Node[], HeadTail) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with all the variables from the supplied nodes.
Table(Node, Integer) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with the specified node variable at the specified time.
Table(Table) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class, copying the [table] passed in.
Table(Table, boolean) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class, with the same structure as an existing [table], copying the values if requested.
Table(Table, boolean, Integer) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class, with the same structure as an existing [table], copying the values if requested, and optionally shifting any times.
Table(Table, Integer) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class, copying the [table] passed in, however adjusting any times by the [timeShift].
Table(Variable) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with a single Variable.
Table(Variable...) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with the specified variables.
Table(VariableContext) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class from a single VariableContext.
Table(VariableContext[]) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(VariableContext[], int) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with [count] variable contexts taken from [buffer].
Table(VariableContext[], int, HeadTail) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with [count] variable contexts taken from [buffer].
Table(VariableContextCollection) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with the variables specified in [variableContexts].
Table(Variable, Integer) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with a single Variable and time.
Table(List<Variable>, Integer) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with the specified variables, at an optional time.
Table(List<Variable>, Integer, HeadTail) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with the specified variables, at an optional time.
Table(List<VariableContext>) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(List<VariableContext>, HeadTail) - Constructor for class com.bayesserver.Table: Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table.MarginalizeLowMemoryOptions - Class in com.bayesserver: Options controlling Table.marginalizeLowMemory(com.bayesserver.Table[]).
Table.MaxValue - Class in com.bayesserver
Table.NonZeroValues - Interface in com.bayesserver: Used to report non zero table values.
TableAccessor - Class in com.bayesserver: Allows random access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in Table.getSortedVariables().
TableAccessor(Table, Node[]) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the node variables.
TableAccessor(Table, Node[], Integer[]) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the node variables.
TableAccessor(Table, Variable[]) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableAccessor(Table, Variable[], Integer[]) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables at specified times.
TableAccessor(Table, VariableContextCollection) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableAccessor(Table, List<Variable>, List<Integer>) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables at specified times.
TableAccessor(Table, List<VariableContext>) - Constructor for class com.bayesserver.TableAccessor: Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableExpression - Class in com.bayesserver: Represents an expression that is used to generate Table distributions.
TableExpression(String) - Constructor for class com.bayesserver.TableExpression: Constructs a new TableExpression instance with double return type.
TableExpression(String, ExpressionReturnType) - Constructor for class com.bayesserver.TableExpression: Constructs a new TableExpression instance.
TableExpression(String, ExpressionReturnType, TableExpressionNormalization) - Constructor for class com.bayesserver.TableExpression: Constructs a new TableExpression instance.
TableExpressionNormalization - Enum in com.bayesserver: The type of normalization to apply to a table (if any) once an expression has generated the values.
TableIterator - Class in com.bayesserver: Allows sequential access to the values in a Table, using a preferred variable ordering, as opposed to the default sorted order specified in Table.getSortedVariables().
TableIterator(Table, Node[]) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
TableIterator(Table, Node[], Integer[]) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
TableIterator(Table, Variable[]) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables.
TableIterator(Table, Variable[], Integer[]) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables at specified times.
TableIterator(Table, VariableContextCollection) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables.
TableIterator(Table, List<Variable>, List<Integer>) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the variables at specified times.
TableIterator(Table, List<VariableContext>) - Constructor for class com.bayesserver.TableIterator: Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
TAIL - com.bayesserver.HeadTail: Indicates that a variable is marked as tail in a distribution.
takeSample(Evidence, RandomNumberGenerator, DataSamplingOptions) - Method in class com.bayesserver.data.sampling.DataSampler: Generates sample data from the Bayesian network or Dynamic Bayesian network.
TANLinkOutput - Class in com.bayesserver.learning.structure: Contains information about a new link learnt using the com.bayesserver.learning.structure.tan.TANStructuralLearning algorithm.
TANStructuralLearning - Class in com.bayesserver.learning.structure: A structural learning algorithm for Bayesian networks based on the Tree augmented naive Bayes (TAN) algorithm.
TANStructuralLearning() - Constructor for class com.bayesserver.learning.structure.TANStructuralLearning
TANStructuralLearningOptions - Class in com.bayesserver.learning.structure: Options for structural learning with the com.bayesserver.learning.structure.tan.TANStructuralLearning class.
TANStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.TANStructuralLearningOptions
TANStructuralLearningOutput - Class in com.bayesserver.learning.structure: Contains information returned from the com.bayesserver.learning.structure.tan.TANStructuralLearning algorithm.
TANStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure: Progress information returned from the TAN structural learning algorithm.
TARGET - com.bayesserver.optimization.ObjectiveKind: The objective should be as close to a target value as possible
TEMPORAL - com.bayesserver.inference.EvidenceType: The variable has evidence at one or more times.
TEMPORAL - com.bayesserver.TemporalType: A temporal node, which can be queried or have evidence set at each time step.
TemporalReaderOptions - Class in com.bayesserver.data: Options that apply to the reading of temporal data.
TemporalReaderOptions(String, String, TimeValueType) - Constructor for class com.bayesserver.data.TemporalReaderOptions: Initializes a new instance of the TemporalReaderOptions class.
TemporalReadInfo - Class in com.bayesserver.data: Provides information about a temporal record.
TemporalReadInfo(Integer, Object, DataRecord) - Constructor for class com.bayesserver.data.TemporalReadInfo: Initializes a new instance of the TemporalReadInfo class.
TemporalType - Enum in com.bayesserver: The node type for networks that include temporal/sequential support.
TERMINAL - com.bayesserver.TemporalType: A node which cannot link to temporal nodes except for the last time slice.
test(DataPartitioning, CrossValidationNetwork) - Method in interface com.bayesserver.data.CrossValidationActions: A user supplied function to test the network on a test partitioning of the data.
test(Evidence) - Method in class com.bayesserver.analysis.InSampleAnomalyDetection: Determines whether a record is anomalous.
THROW_EXCEPTION - com.bayesserver.data.EmptyStringAction: Throw an exception.
THROW_EXCEPTION - com.bayesserver.data.StateNotFoundAction: Throw an exception.
THROW_EXCEPTION - com.bayesserver.learning.structure.LinkConstraintFailureMode: If the link constraint cannot be honoured, throw an exception.
TimeSeriesMode - Enum in com.bayesserver.learning.parameters: Determines how time series distributions are learned.
timeShift(int) - Method in class com.bayesserver.CLGaussian: Shifts any times associated with the distribution variables by the specified number of time units.
timeShift(int) - Method in interface com.bayesserver.Distribution: Shifts any times associated with the distribution variables by the specified number of time units.
timeShift(int) - Method in class com.bayesserver.Table: Shifts any times associated with the table variables by the specified number of units.
TimeValueType - Enum in com.bayesserver.data: The type of values stored in a time column.
TopologicalSort - Class in com.bayesserver: Contains methods to sort nodes in a Bayesian network in topological order.
TopologicalSortNodeInfo - Class in com.bayesserver: Information about the topological order of a node.
toString() - Method in class com.bayesserver.causal.CausalNode
toString() - Method in class com.bayesserver.CLGaussian
toString() - Method in class com.bayesserver.data.discovery.DiscretizationOptions
toString() - Method in class com.bayesserver.inference.AssignedDefinition
toString() - Method in class com.bayesserver.inference.CliqueDefinition
toString() - Method in class com.bayesserver.inference.EliminationDefinition: >
toString() - Method in class com.bayesserver.inference.QueryDistribution: Returns a String that represents this instance.
toString() - Method in class com.bayesserver.inference.SepsetDefinition
toString() - Method in class com.bayesserver.Interval
toString() - Method in class com.bayesserver.learning.parameters.InitializationOptions
toString() - Method in class com.bayesserver.learning.parameters.Priors: Returns a String that represents this instance.
toString() - Method in class com.bayesserver.Node: Returns the name of the node, or an empty string if the name is null.
toString() - Method in class com.bayesserver.optimization.GeneticTerminationOptions
toString() - Method in class com.bayesserver.State: Returns the name of the state, or an empty string if the name is null.
toString() - Method in class com.bayesserver.Table
toString() - Method in class com.bayesserver.Variable: Returns the name of the variable, or an empty string if the name is null.
toString() - Method in class com.bayesserver.VariableContextCollection: Returns a String that represents the current Object.
TOTAL - com.bayesserver.inference.CausalEffectKind: The total causal effect, which includes effects on non-direct causal paths between treatments (X) and outcomes (Y).
TreeQuery - Class in com.bayesserver.inference: Contains methods to determine properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
TreeQueryOptions - Class in com.bayesserver.inference: Options which affect the calculation performed by a TreeQuery.
TreeQueryOptions() - Constructor for class com.bayesserver.inference.TreeQueryOptions: Initializes a new instance of the TreeQueryOptions class.
TreeQueryOptions(QueryOptions) - Constructor for class com.bayesserver.inference.TreeQueryOptions: Initializes a new instance of the TreeQueryOptions class, copying options from another instance implementing QueryOptions.
TreeQueryOutput - Class in com.bayesserver.inference: Contains information output by a TreeQuery.
TWO - com.bayesserver.statistics.LogarithmBase: Base 2 logarithm.
twoWay(Evidence, State, ParameterReference, ParameterReference) - Method in class com.bayesserver.analysis.SensitivityToParameters: Calculates how a hypothesis varies based on changes to two parameters.

U

UNIFORM - com.bayesserver.learning.parameters.DecisionPostProcessingMethod: The distribution of each decision node is overwritten with a uniform distribution, but only once learning is complete.
UNIFORM - com.bayesserver.learning.parameters.DiscretePriorMethod: The prior is uniformly distributed over all discrete combinations.
UNOBSERVED - com.bayesserver.CausalObservability: The causal node is unobserved.
unroll(Network, int, UnrollOptions) - Static method in class com.bayesserver.Unroller: Unrolls the specified Dynamic Bayesian network into the equivalent Bayesian network.
Unroller - Class in com.bayesserver: Unrolls a Dynamic Bayesian network into the equivalent Bayesian network.
UnrollOptions - Class in com.bayesserver: Options governing the unrolling of a Dynamic Bayesian network.
UnrollOptions() - Constructor for class com.bayesserver.UnrollOptions
UnrollOutput - Class in com.bayesserver: Contains information returned by Unroller.unroll(com.bayesserver.Network, int, com.bayesserver.UnrollOptions).
UnrollOutput.NodeTime - Class in com.bayesserver: Identifies a node and related time.
UnrollOutput.VariableTime - Class in com.bayesserver: Identifies a variable and related time.
UNWEIGHTED_SUM - com.bayesserver.data.CrossValidationCombineMethod: Simply sums the numeric test results.
update(DiscretizeProgressInfo) - Method in interface com.bayesserver.data.discovery.DiscretizeProgress: Progress updates from a discretization algorithm.
update(VariableGeneratorProgressInfo) - Method in interface com.bayesserver.data.discovery.VariableGeneratorProgress: Progress updates from the Variable Generator algorithm.
update(Evidence, List<Variable>, List<Variable>, AbductionOptions) - Static method in class com.bayesserver.causal.Abduction: Performs abduction which is one of the steps in 'counterfactual analysis'.
update(ParameterLearningProgressInfo) - Method in interface com.bayesserver.learning.parameters.ParameterLearningProgress: Progress update, containing information about the last iteration.
update(StructuralLearningProgressInfo) - Method in interface com.bayesserver.learning.structure.StructuralLearningProgress: Progress updates from the structural learning algorithm.
update(OptimizerProgressInfo) - Method in interface com.bayesserver.optimization.OptimizerProgress: Progress updates from the optimization algorithm.
UTILITY - com.bayesserver.VariableKind: A utility variable, which can be used to encode utilities such as costs and profits.

V

validate(Evidence, Distribution, ValidationOptions) - Method in class com.bayesserver.causal.BackdoorCriterion: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(Evidence, Distribution, ValidationOptions) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(Evidence, Distribution, ValidationOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(Evidence, Distribution, ValidationOptions) - Method in interface com.bayesserver.causal.Validation: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(ValidationOptions) - Method in class com.bayesserver.Network: Validates that the Bayesian network is correctly specified.
validate(String) - Static method in class com.bayesserver.License: Validates the library.
validate(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in class com.bayesserver.causal.BackdoorCriterion: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in class com.bayesserver.causal.FrontDoorCriterion: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validate(List<CausalNode>, List<CausalNode>, List<CausalNode>, ValidationOptions) - Method in interface com.bayesserver.causal.Validation: Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
validateDistribution(Distribution, NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions: Checks that a distribution is correctly specified for a particular temporal order.
validateDistribution(Distribution, NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions: Checks that a distribution is correctly specified for a particular temporal order.
validateExpression(DistributionExpression, NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions: Determines whether an expression is valid for the given key and kind, without having to assign it to a node.
validateSyntax(String) - Static method in class com.bayesserver.FunctionVariableExpression: Validates the syntax of a function expression.
validateTrialSession() - Static method in class com.bayesserver.Network: Evaluation version only.
Validation - Interface in com.bayesserver.causal: Methods to test whether adjustment inputs are valid.
ValidationException - Exception in com.bayesserver.causal: Raised by an identification algorithm when validation fails.
ValidationException() - Constructor for exception com.bayesserver.causal.ValidationException: Initializes a new instance of the ValidationException class.
ValidationException(String) - Constructor for exception com.bayesserver.causal.ValidationException: Initializes a new instance of the ValidationException class with a specified error message.
ValidationException(String, Throwable) - Constructor for exception com.bayesserver.causal.ValidationException: Initializes a new instance of the ValidationException class with a specified error message and a reference to the inner exception that is the cause of this exception.
ValidationException(Throwable) - Constructor for exception com.bayesserver.causal.ValidationException: Initializes a new instance of the ValidationException class with a reference to the inner exception that is the cause of this exception.
ValidationOptions - Class in com.bayesserver: Represents options that govern the validation of a network.
ValidationOptions - Interface in com.bayesserver.causal: Options for classes that implement Validation
ValidationOptions() - Constructor for class com.bayesserver.ValidationOptions
value(int, double) - Method in interface com.bayesserver.Table.NonZeroValues: Called for each non zero value in the table.
VALUE - com.bayesserver.data.ColumnValueType: The column contains the value of a continuous variable or a value to match to a discrete State.getValue().
VALUE - com.bayesserver.data.TimeValueType: The time column contains values that can be sorted, and will be treated in sequence.
valueOf(String) - Static method in enum com.bayesserver.analysis.AutoInsightJSDivergence: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.AutoInsightKLDivergence: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.DSeparationCategory: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.ImpactSubsetMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.LogLikelihoodAnalysisSubsetMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.analysis.ValueOfInformationKind: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.causal.BackdoorMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.CausalObservability: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.CollectionAction: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.ColumnValueType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.CrossValidationCombineMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.DataPartitionMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.discovery.DiscretizationMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.discovery.SortOrder: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.EmptyStringAction: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.StateNotFoundAction: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.data.TimeValueType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.ExpressionDistribution: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.ExpressionReturnType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.HeadTail: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.CausalEffectKind: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.DecisionAlgorithm: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.EvidenceType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.InconsistentEvidenceMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.InterventionType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.QueryComparison: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.QueryDistance: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.inference.QueryEvidenceMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.IntervalEndPoint: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.ConvergenceMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.DecisionPostProcessingMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.DiscretePriorMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.DistributionMonitoring: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.InitializationMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.parameters.TimeSeriesMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.structure.LinkConstraintFailureMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.structure.LinkConstraintMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.learning.structure.ScoreMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.NodeDistributionKind: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.NoisyOrder: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.NoisyType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.optimization.DesignEvidenceKind: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.optimization.ObjectiveKind: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.PropagationMethod: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.StateValueType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.statistics.LogarithmBase: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.TableExpressionNormalization: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.TemporalType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.VariableKind: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum com.bayesserver.VariableValueType: Returns the enum constant of this type with the specified name.
ValueOfInformation - Class in com.bayesserver.analysis: Contains methods to determine what new evidence is most likely to reduce the uncertainty of a variable.
ValueOfInformationKind - Enum in com.bayesserver.analysis: The type of value of information statistic calculated.
ValueOfInformationOptions - Class in com.bayesserver.analysis: Options for calculating ValueOfInformation.
ValueOfInformationOptions() - Constructor for class com.bayesserver.analysis.ValueOfInformationOptions: Initializes a new instance of the ValueOfInformationOptions class.
ValueOfInformationOutput - Class in com.bayesserver.analysis: Contains the results of the tests carried out using ValueOfInformation.
ValueOfInformationTestOutput - Class in com.bayesserver.analysis: Contains information about a variable tested via ValueOfInformation.
values() - Static method in enum com.bayesserver.analysis.AutoInsightJSDivergence: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.AutoInsightKLDivergence: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.DSeparationCategory: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.ImpactSubsetMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.LogLikelihoodAnalysisSubsetMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.analysis.ValueOfInformationKind: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.causal.BackdoorMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.CausalObservability: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.CollectionAction: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.ColumnValueType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.CrossValidationCombineMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.DataPartitionMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.discovery.DiscretizationMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.discovery.SortOrder: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.EmptyStringAction: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.StateNotFoundAction: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.data.TimeValueType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.ExpressionDistribution: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.ExpressionReturnType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.HeadTail: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.CausalEffectKind: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.DecisionAlgorithm: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.EvidenceType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.InconsistentEvidenceMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.InterventionType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.QueryComparison: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.QueryDistance: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.inference.QueryEvidenceMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.IntervalEndPoint: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.ConvergenceMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.DecisionPostProcessingMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.DiscretePriorMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.DistributionMonitoring: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.InitializationMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.parameters.TimeSeriesMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.structure.LinkConstraintFailureMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.structure.LinkConstraintMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.learning.structure.ScoreMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.NodeDistributionKind: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.NoisyOrder: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.NoisyType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.optimization.DesignEvidenceKind: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.optimization.ObjectiveKind: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.PropagationMethod: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.StateValueType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.statistics.LogarithmBase: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.TableExpressionNormalization: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.TemporalType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.VariableKind: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum com.bayesserver.VariableValueType: Returns an array containing the constants of this enum type, in the order they are declared.
Variable - Class in com.bayesserver: Represents a discrete or continuous random variable.
Variable() - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class, with VariableValueType discrete and zero states.
Variable(String) - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class, with VariableValueType discrete, zero states, and the specified name.
Variable(String, int) - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class, with VariableValueType discrete and the specified [name] and adds the number of states specified in [states].
Variable(String, State...) - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class, with VariableValueType discrete and the specified name and adds the states specified in [states].
Variable(String, VariableValueType) - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class with the specified name and value type.
Variable(String, VariableValueType, VariableKind) - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class with the specified name, kind and value type.
Variable(String, String[]) - Constructor for class com.bayesserver.Variable: Initializes a new instance of the Variable class, with VariableValueType discrete and the specified name and adds the states specified in [states].
variableCollectionChange(int, Variable, Variable, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor: For internal use.
VariableContext - Class in com.bayesserver: Represents a variable and associated information such as time, and whether it is marked as head or tail.
VariableContext(Variable) - Constructor for class com.bayesserver.VariableContext: Initializes a new instance of the VariableContext class.
VariableContext(VariableContext) - Constructor for class com.bayesserver.VariableContext: Initializes a new instance of the VariableContext class, copying an existing instance.
VariableContext(Variable, HeadTail) - Constructor for class com.bayesserver.VariableContext: Initializes a new instance of the VariableContext class.
VariableContext(Variable, Integer) - Constructor for class com.bayesserver.VariableContext: Initializes a new instance of the VariableContext class.
VariableContext(Variable, Integer, HeadTail) - Constructor for class com.bayesserver.VariableContext: Initializes a new instance of the VariableContext class.
VariableContextCollection - Class in com.bayesserver: Represents a read-only collection of variables.
VariableDefinition - Class in com.bayesserver.data.discovery: Defines how a variable should be created.
VariableDefinition() - Constructor for class com.bayesserver.data.discovery.VariableDefinition: Initializes a new instance of the VariableDefinition class.
VariableDefinition(String, String, VariableValueType) - Constructor for class com.bayesserver.data.discovery.VariableDefinition: Initializes a new instance of the VariableDefinition class.
VariableDefinition(String, String, VariableValueType, StateValueType) - Constructor for class com.bayesserver.data.discovery.VariableDefinition: Initializes a new instance of the VariableDefinition class.
VariableDefinition(String, String, VariableValueType, StateValueType, VariableKind) - Constructor for class com.bayesserver.data.discovery.VariableDefinition: Initializes a new instance of the VariableDefinition class.
VariableEliminationInference - Class in com.bayesserver.inference: An exact inference algorithm for Bayesian networks and Dynamic Bayesian networks, loosely based on the Variable Elimination algorithm.
VariableEliminationInference(Network) - Constructor for class com.bayesserver.inference.VariableEliminationInference: Initializes a new instance of the VariableEliminationInference class, with the target Bayesian network.
VariableEliminationInferenceFactory - Class in com.bayesserver.inference: Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
VariableEliminationInferenceFactory() - Constructor for class com.bayesserver.inference.VariableEliminationInferenceFactory
VariableEliminationQueryLifecycleBegin - Class in com.bayesserver.inference: Query lifecycle begin implementation for the Variable Elimination algorithm.
VariableEliminationQueryLifecycleEnd - Class in com.bayesserver.inference: Query end lifecycle implementation for the Variable Elimination algorithm.
VariableEliminationQueryOptions - Class in com.bayesserver.inference: Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
VariableEliminationQueryOptions() - Constructor for class com.bayesserver.inference.VariableEliminationQueryOptions: Initializes a new instance of the VariableEliminationQueryOptions class.
VariableEliminationQueryOutput - Class in com.bayesserver.inference: Returns any information, in addition to the distributions, that is requested from a query.
VariableEliminationQueryOutput() - Constructor for class com.bayesserver.inference.VariableEliminationQueryOutput: Initializes a new instance of the VariableEliminationQueryOutput class.
VariableGenerator - Class in com.bayesserver.data.discovery: Generates variables from a data source.
VariableGeneratorOptions - Class in com.bayesserver.data.discovery: Options that affect the generation of variables from data.
VariableGeneratorOptions() - Constructor for class com.bayesserver.data.discovery.VariableGeneratorOptions
VariableGeneratorProgress - Interface in com.bayesserver.data.discovery: Interface to provide progress information during data discovery (VariableGenerator).
VariableGeneratorProgressInfo - Class in com.bayesserver.data.discovery: Interface to provide progress information during data discovery (VariableGenerator).
VariableInfo - Class in com.bayesserver.data.discovery: Contains the generated Variable and any supplementary information.
VariableInfoCount - Class in com.bayesserver.data.discovery: Reports weighted and unweighted record counts.
VariableInfoCounts - Class in com.bayesserver.data.discovery: Reports counts for each variable.
VariableInfoValue - Class in com.bayesserver.data.discovery: Reports general weighted and unweighted information/statistics about a variable.
VariableKind - Enum in com.bayesserver: The kind of variable, such as Probability, Decision or Utility.
VariableMap - Class in com.bayesserver: Maps between a custom variable order and the default sorted variable order.
VariableMap(VariableContextCollection, Node[]) - Constructor for class com.bayesserver.VariableMap: Initializes a new instance of the VariableMap class.
VariableMap(VariableContextCollection, List<Variable>, List<Integer>) - Constructor for class com.bayesserver.VariableMap: Initializes a new instance of the VariableMap class.
VariableMap(VariableContextCollection, List<VariableContext>) - Constructor for class com.bayesserver.VariableMap: Initializes a new instance of the VariableMap class.
VariableReference - Class in com.bayesserver.data: Identifies a Variable and data binding information.
VariableReference(Variable, ColumnValueType, String) - Constructor for class com.bayesserver.data.VariableReference: Initializes a new instance of the VariableReference class.
VariableReference(Variable, ColumnValueType, String, StateNotFoundAction) - Constructor for class com.bayesserver.data.VariableReference: Initializes a new instance of the VariableReference class.
VariableReference(Variable, ColumnValueType, String, StateNotFoundAction, EmptyStringAction) - Constructor for class com.bayesserver.data.VariableReference: Initializes a new instance of the VariableReference class.
VariableReference(Variable, ColumnValueType, String, StateNotFoundAction, EmptyStringAction, String) - Constructor for class com.bayesserver.data.VariableReference: Initializes a new instance of the VariableReference class.
VariableValueType - Enum in com.bayesserver: The type of data represented by a Variable.

W

WEIGHTED_AVERAGE - com.bayesserver.data.CrossValidationCombineMethod: Calculates the average of the numeric test results weighted by the number of records in each test partitioning.
WeightedValue - Class in com.bayesserver.data.discovery: A value (which can be null) and its associated weight (support).
WeightedValue() - Constructor for class com.bayesserver.data.discovery.WeightedValue
WindowDataReader - Class in com.bayesserver.data.timeseries: A data reader that reads windows of data over another data reader.
WindowDataReader(DataReader, WindowOptions, WindowDataReaderOptions) - Constructor for class com.bayesserver.data.timeseries.WindowDataReader: Initializes a new instance of the WindowDataReader class.
WindowDataReaderCommand - Class in com.bayesserver.data.timeseries: A data reader command that reads windows of data over another data reader.
WindowDataReaderCommand(DataReaderCommand, WindowOptions, WindowDataReaderOptions) - Constructor for class com.bayesserver.data.timeseries.WindowDataReaderCommand: Initializes a new instance of the WindowDataReaderCommand class.
WindowDataReaderOptions - Class in com.bayesserver.data.timeseries: Options for creating windowed data readers.
WindowDataReaderOptions() - Constructor for class com.bayesserver.data.timeseries.WindowDataReaderOptions
WindowOptions - Class in com.bayesserver.data.timeseries: Options for creating windows over time series data.
WindowOptions(int[], int) - Constructor for class com.bayesserver.data.timeseries.WindowOptions: Initializes a new instance of the WindowOptions class.
WindowOptions(int, int) - Constructor for class com.bayesserver.data.timeseries.WindowOptions: Initializes a new instance of the WindowOptions class, with a shift of 1.
WindowOptions(int, int, int) - Constructor for class com.bayesserver.data.timeseries.WindowOptions: Initializes a new instance of the WindowOptions class.
write(OutputStream) - Method in interface com.bayesserver.WriteStreamAction: Write to a the stream.
write(String, WriteStreamAction) - Method in interface com.bayesserver.NameValuesWriter: Write a value for a name.
WriteStreamAction - Interface in com.bayesserver: Provides an output stream that can be written to.

Z

zeroAll() - Method in class com.bayesserver.learning.parameters.Priors: Sets all values to zero.

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