Uses of Interface
com.bayesserver.inference.Evidence

Packages that use Evidence

Package	Description
com.bayesserver.analysis
com.bayesserver.causal
com.bayesserver.data
com.bayesserver.data.sampling
com.bayesserver.inference
com.bayesserver.learning.parameters
com.bayesserver.optimization

Uses of Evidence in com.bayesserver.analysis

Methods in com.bayesserver.analysis with parameters of type Evidence

Modifier and Type	Method	Description
static AssociationOutput	Association.calculate(List<AssociationPair> pairs, Evidence evidence, AssociationOptions options)	Calculates the association/information between two sets of variables, such as those at either end of a Link.
static AutoInsightOutput	AutoInsight.calculate(State target, List<Variable> testVariables, Evidence evidence, AutoInsightOptions options)	Uses comparison queries to automatically derive insight about a target variable from a trained network.
static AutoInsightOutput	AutoInsight.calculate(State target, List<Variable> testVariables, InferenceFactory factory, Evidence evidence)	Uses comparison queries to automatically derive insight about a target variable from a trained network.
static AutoInsightOutput[]	AutoInsight.calculate(Variable continuousTarget, List<Interval<Double>> targetIntervals, List<Variable> testVariables, Evidence evidence, AutoInsightOptions options)	Uses comparison queries to automatically derive insight about a target variable from a trained network.
static DSeparationOutput	DSeparation.calculate(Network network, List<Node> sourceNodes, List<Node> testNodes, Evidence evidence, DSeparationOptions options)	Calculates whether sets of nodes are D-Separated, given any evidence.
static DSeparationOutput	DSeparation.calculate(Network network, List<Node> sourceNodes, List<Integer> sourceNodeTimes, List<Node> testNodes, List<Integer> testTimes, Evidence evidence, DSeparationOptions options)	Calculates whether sets of nodes are D-Separated, given any evidence, and associated times for any temporal nodes.
static ImpactOutput	Impact.calculate(Network network, Distribution hypothesisQuery, Evidence evidence, List<Variable> evidenceToAnalyse, ImpactOptions options)	Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
static ImpactOutput	Impact.calculate(Network network, Distribution hypothesisQuery, StateContext[] hypothesisCombination, Evidence evidence, List<Variable> evidenceToAnalyse, ImpactOptions options)	Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
static ImpactOutput	Impact.calculate(Network network, Variable hypothesisVariable, Evidence evidence, List<Variable> evidenceToAnalyse, ImpactOptions options)	Analyzes the impact of sets of evidence on a hypothesis state and its variable.
static ImpactOutput	Impact.calculate(Network network, Variable hypothesisVariable, State hypothesisState, Evidence evidence, List<Variable> evidenceToAnalyse, ImpactOptions options)	Analyzes the impact of sets of evidence on a hypothesis state and its variable.
static LogLikelihoodAnalysisOutput	LogLikelihoodAnalysis.calculate(Network network, Evidence evidence, List<Variable> evidenceToAnalyse, LogLikelihoodAnalysisOptions options)	Analyzes the log-likelihood based on subsets of evidence.
static ValueOfInformationOutput	ValueOfInformation.calculate(VariableContext hypothesis, List<VariableContext> testVariables, Evidence evidence, InferenceFactory factory, ValueOfInformationOptions options)	Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
static ValueOfInformationOutput	ValueOfInformation.calculate(Variable hypothesis, List<Variable> testVariables, Evidence evidence, InferenceFactory factory, ValueOfInformationOptions options)	Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
static ImpactHypothesisOutput	Impact.calculateStreamed(Network network, Distribution hypothesisQuery, Evidence evidence, List<Variable> evidenceToAnalyse, ImpactAction outputItem, ImpactOptions options)	Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
static ImpactHypothesisOutput	Impact.calculateStreamed(Network network, Distribution hypothesisQuery, StateContext[] hypothesisState, Evidence evidence, List<Variable> evidenceToAnalyse, ImpactAction outputItem, ImpactOptions options)	Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
static LogLikelihoodAnalysisBaselineOutput	LogLikelihoodAnalysis.calculateStreamed(Network network, Evidence evidence, List<Variable> evidenceToAnalyse, LogLikelihoodAnalysisAction outputItem, LogLikelihoodAnalysisOptions options)	Analyzes the log-likelihood based on subsets of evidence.
SensitivityFunctionOneWay	SensitivityToParameters.oneWay(Evidence evidence, State hypothesis, ParameterReference parameter)	Calculates how a hypothesis varies based on changes to a single parameter.
InSampleAnomalyDetectionOutput	InSampleAnomalyDetection.test(Evidence evidence)	Determines whether a record is anomalous.
SensitivityFunctionTwoWay	SensitivityToParameters.twoWay(Evidence evidence, State hypothesis, ParameterReference parameter1, ParameterReference parameter2)	Calculates how a hypothesis varies based on changes to two parameters.

Uses of Evidence in com.bayesserver.causal

Methods in com.bayesserver.causal that return Evidence

Modifier and Type	Method	Description
Evidence	CausalInferenceBase.getBaseEvidence()	Optional evidence which can be used to calculate the lift of queries.
Evidence	CausalInferenceBase.getEvidence()	Represents the evidence, or case data (e.g.

Methods in com.bayesserver.causal with parameters of type Evidence

Modifier and Type	Method	Description
static EffectsAnalysisOutput	EffectsAnalysis.calculate(Variable treatment, Variable outcome, CausalEffectKind effect, Evidence fixedEvidence, InferenceFactory factory, EffectsAnalysisOptions options)	Calculate the causal effect on a target, varying for different treatment values.
static void	BackdoorGraph.convert(Network network, Evidence evidence, Distribution query, BackdoorGraphOptions options)	Constructs the Backdoor graph or the proper Backdoor graph from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
static void	IndirectGraph.convert(Network network, Evidence evidence, Distribution query, IndirectGraphOptions options)	Constructs the 'Indirect graph' from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
IdentificationOutput	BackdoorCriterion.identify(Evidence evidence, Distribution query, IdentificationOptions options)	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOutput	DisjunctiveCauseCriterion.identify(Evidence evidence, Distribution query, IdentificationOptions options)	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOutput	FrontDoorCriterion.identify(Evidence evidence, Distribution query, IdentificationOptions options)	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOutput	Identification.identify(Evidence evidence, Distribution query, IdentificationOptions options)	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
BackdoorCriterionOutput	FrontDoorCriterion.identifyXZ(Evidence evidence, FrontDoorSet frontDoorNodes, BackdoorCriterionOptions options)	Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).
BackdoorCriterionOutput	FrontDoorCriterion.identifyZY(Evidence evidence, FrontDoorSet frontDoorNodes, Distribution query, BackdoorCriterionOptions options)	Uses the 'Backdoor criterion' to identify any 'adjustment sets' between front-door nodes (Z) and outcomes (Y).
boolean	BackdoorCriterion.isValid(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, without raising an exception.
boolean	DisjunctiveCauseCriterion.isValid(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, without raising an exception.
boolean	FrontDoorCriterion.isValid(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, without raising an exception.
boolean	Validation.isValid(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, without raising an exception.
void	CausalInferenceBase.setBaseEvidence(Evidence value)	Optional evidence which can be used to calculate the lift of queries.
void	CausalInferenceBase.setEvidence(Evidence value)	Represents the evidence, or case data (e.g.
static void	Abduction.update(Evidence evidence, List<Variable> abductionEvidenceVariables, List<Variable> characteristicVariables, AbductionOptions options)	Performs abduction which is one of the steps in 'counterfactual analysis'.
void	BackdoorCriterion.validate(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
void	DisjunctiveCauseCriterion.validate(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
void	FrontDoorCriterion.validate(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
void	Validation.validate(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.

Uses of Evidence in com.bayesserver.data

Methods in com.bayesserver.data with parameters of type Evidence

Modifier and Type	Method	Description
boolean	DefaultEvidenceReader.read(Evidence evidence, ReadOptions readOptions)	Reads the next case (record).
boolean	EvidenceReader.read(Evidence evidence, ReadOptions readOptions)	Reads the next case (record).
boolean	DefaultEvidenceReader.readTemporal(Evidence evidence, ReadOptions readOptions)	Reads the next temporal record, setting evidence.

Uses of Evidence in com.bayesserver.data.sampling

Methods in com.bayesserver.data.sampling with parameters of type Evidence

Modifier and Type	Method	Description
void	DataSampler.setFixedData(Evidence value)	Sets any evidence that should be fixed for each sample.
void	DataSampler.takeSample(Evidence sampleData, RandomNumberGenerator random, DataSamplingOptions options)	Generates sample data from the Bayesian network or Dynamic Bayesian network.

Constructors in com.bayesserver.data.sampling with parameters of type Evidence

Constructor	Description
DataSampler(Network network, Evidence fixedData)	Initializes a new instance of the DataSampler class.

Uses of Evidence in com.bayesserver.inference

Classes in com.bayesserver.inference that implement Evidence

Modifier and Type	Class	Description
class	DefaultEvidence	Represents the evidence, or case data (e.g.

Methods in com.bayesserver.inference that return Evidence

Modifier and Type	Method	Description
Evidence	Inference.getBaseEvidence()	Optional evidence which can be used to calculate the lift of queries.
Evidence	LikelihoodSamplingInference.getBaseEvidence()	Optional evidence which can be used to calculate the lift of queries.
Evidence	LoopyBeliefInference.getBaseEvidence()	Optional evidence which can be used to calculate the lift of queries.
Evidence	RelevanceTreeInference.getBaseEvidence()	Optional evidence which can be used to calculate the lift of queries.
Evidence	VariableEliminationInference.getBaseEvidence()	Optional evidence which can be used to calculate the lift of queries.
Evidence	Inference.getEvidence()	Represents the evidence, or case data (e.g.
Evidence	LikelihoodSamplingInference.getEvidence()	Represents the evidence, or case data (e.g.
Evidence	LoopyBeliefInference.getEvidence()	Represents the evidence, or case data (e.g.
Evidence	RelevanceTreeInference.getEvidence()	Represents the evidence, or case data (e.g.
Evidence	VariableEliminationInference.getEvidence()	Gets the evidence (case data, e.g.

Methods in com.bayesserver.inference with parameters of type Evidence

Modifier and Type	Method	Description
void	DefaultEvidence.copy(Evidence evidence)	Replaces the current evidence, with that from another Evidence instance.
void	DefaultEvidence.copy(Evidence evidence, Variable variable)	Replaces the current evidence for an individual variable, with that from another Evidence instance.
void	DefaultEvidence.copy(Evidence evidence, Variable variable, Integer time)	Replaces the current evidence for an individual variable at a specific time, with that from another Evidence instance.
void	Evidence.copy(Evidence evidence)	Replaces the current evidence, with that from another Evidence instance.
void	Evidence.copy(Evidence evidence, Variable variable)	Replaces the current evidence for an individual variable, with that from another Evidence instance.
void	Evidence.copy(Evidence evidence, Variable variable, Integer time)	Replaces the current evidence for an individual variable at a specific time, with that from another Evidence instance.
static TreeQueryOutput	TreeQuery.query(Network network, QueryDistributionCollection queryDistributions, Evidence evidence, TreeQueryOptions queryOptions)	Calculates properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
void	Inference.setBaseEvidence(Evidence value)	Optional evidence which can be used to calculate the lift of queries.
void	LikelihoodSamplingInference.setBaseEvidence(Evidence value)	Optional evidence which can be used to calculate the lift of queries.
void	LoopyBeliefInference.setBaseEvidence(Evidence value)	Optional evidence which can be used to calculate the lift of queries.
void	RelevanceTreeInference.setBaseEvidence(Evidence value)	Optional evidence which can be used to calculate the lift of queries.
void	VariableEliminationInference.setBaseEvidence(Evidence value)	Optional evidence which can be used to calculate the lift of queries.
void	Inference.setEvidence(Evidence value)	Represents the evidence, or case data (e.g.
void	LikelihoodSamplingInference.setEvidence(Evidence value)	Represents the evidence, or case data (e.g.
void	LoopyBeliefInference.setEvidence(Evidence value)	Represents the evidence, or case data (e.g.
void	RelevanceTreeInference.setEvidence(Evidence value)	Represents the evidence, or case data (e.g.
void	VariableEliminationInference.setEvidence(Evidence value)	Sets the evidence (case data, e.g.

Constructors in com.bayesserver.inference with parameters of type Evidence

Constructor	Description
DefaultEvidence(Evidence evidence)	Initializes a new instance of the DefaultEvidence class, and copies the evidence from another instance.

Uses of Evidence in com.bayesserver.learning.parameters

Methods in com.bayesserver.learning.parameters that return Evidence

Modifier and Type	Method	Description
Evidence	OnlineLearning.getEvidence()	Gets the evidence used internally.

Methods in com.bayesserver.learning.parameters with parameters of type Evidence

Modifier and Type	Method	Description
void	OnlineLearning.adapt(Evidence evidence, OnlineLearningOptions options)	Adapt the parameters of a Bayesian network using Bayesian statistics.

Uses of Evidence in com.bayesserver.optimization

Methods in com.bayesserver.optimization that return Evidence

Modifier and Type	Method	Description
Evidence	GeneticOptimizerOutput.getEvidence()	The evidence required to produce the optimized objective value.
Evidence	GeneticOptimizerProgressInfo.getEvidence()	Gets the evidence for the objective value.
Evidence	GeneticSimplificationOutput.getEvidence()	The evidence required to produce the optimized objective value.
Evidence	OptimizerOutput.getEvidence()	The evidence required to produce the optimized objective value.
Evidence	OptimizerProgressInfo.getEvidence()	Gets the evidence for the objective value.
Evidence	GeneticSimplificationOptions.getEvidenceToSimplify()	The evidence from a previous optimization.

Methods in com.bayesserver.optimization with parameters of type Evidence

Modifier and Type	Method	Description
OptimizerOutput	GeneticOptimizer.optimize(Network network, Objective objective, List<DesignVariable> designVariables, Evidence fixedEvidence, OptimizerOptions options)	Perform optimization of an objective (target).
OptimizerOutput	GeneticSimplification.optimize(Network network, Objective objective, List<DesignVariable> designVariables, Evidence fixedEvidence, OptimizerOptions options)	Perform optimization of an objective (target).
OptimizerOutput	Optimizer.optimize(Network network, Objective objective, List<DesignVariable> designVariables, Evidence fixedEvidence, OptimizerOptions options)	Perform optimization of an objective (target).
void	GeneticSimplificationOptions.setEvidenceToSimplify(Evidence value)	The evidence from a previous optimization.