Uses of Class com.bayesserver.Network (bayesserver 11.0 API)

Packages that use Network
Package	Description
com.bayesserver
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 Network in com.bayesserver

Methods in com.bayesserver that return Network
Modifier and Type	Method	Description
`Network`	Network.`copy()`	Makes a copy of the network.
`Network`	UnrollOutput.`getDbn()`	Gets the Dynamic Bayesian network before it was unrolled.
`Network`	DecomposeOutput.`getDecomposedNetwork()`	Gets the network, which is the decomposed equivalent of the original network.
`Network`	Link.`getNetwork()`	The `Network` the link belongs to.
`Network`	NetworkLinkCollection.`getNetwork()`	Gets the `Network` the collection belongs to.
`Network`	NetworkNodeCollection.`getNetwork()`	The `Network` the collection belongs to.
`Network`	NetworkNodeGroupCollection.`getNetwork()`	Gets the network instance that these groups belong to.
`Network`	NetworkVariableCollection.`getNetwork()`	The `Network` the collection belongs to.
`Network`	Node.`getNetwork()`	The `Network` the node belongs to.
`Network`	DecomposeOutput.`getOriginalNetwork()`	Gets the original network, containing nodes with multiple variables.
`Network`	UnrollOutput.`getUnrolled()`	Gets the unrolled Dynamic Bayesian network.

Methods in com.bayesserver with parameters of type Network
Modifier and Type	Method	Description
`static DecomposeOutput`	Decomposer.`decompose(Network network, DecomposeOptions options)`	Decomposes a Bayesian network containing nodes with multiple variables into its single variable node equivalent.
`static double`	ParameterCounter.`getParameterCount(Network network)`	Gets the number of parameters in a Bayesian network.
`static double`	ParameterCounter.`getParameterCount(Network network, ParameterCountOptions options)`	Gets the number of parameters in a Bayesian network.
`static boolean`	Dag.`isDag(Network network)`	Determines if a network is a Directed Acyclic Graph (DAG).
`static boolean`	Dag.`isDag(Network network, Iterable<Link> ignore, Iterable<Link> extra)`	Determines if a network is a DAG (Directed Acyclic Graph).
`static Node[]`	TopologicalSort.`sort(Network network)`	Returns the nodes in a Bayesian network sorted in topological order.
`static TopologicalSortNodeInfo[]`	TopologicalSort.`sortWithDepth(Network network)`	Returns the nodes in a Bayesian network sorted and grouped in topological order.
`static UnrollOutput`	Unroller.`unroll(Network network, int sliceCount, UnrollOptions options)`	Unrolls the specified Dynamic Bayesian network into the equivalent Bayesian network.

Uses of Network in com.bayesserver.analysis

Methods in com.bayesserver.analysis with parameters of type Network
Modifier and Type	Method	Description
`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 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.
`EvidenceReaderCommand`	ClusterCountActions.`createEvidenceReaderCommand(Network networkCopy, DataPartitioning partitioning)`	A user supplied function to create an evidence reader command based on a copy of the original network.
`static ClusterCountOutput`	ClusterCount.`detect(Network network, Variable cluster, List<Integer> clusterCounts, ClusterCountActions actions, ClusterCountOptions options)`	Determine the number of clusters (discrete states of a latent variable) using cross validation.
`void`	ClusterCountActions.`learn(Network networkCopy, EvidenceReaderCommand evidenceReaderCommand)`	A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.
`static InSampleAnomalyDetection`	InSampleAnomalyDetection.`learn(Network network, EvidenceReaderCommandFactory evidenceReaderCommandFactory, InSampleAnomalyDetectionActions actions, InSampleAnomalyDetectionOptions options)`	Build the in-sample anomaly detector, which can be used to remove anomalous data from a training data set.
`void`	InSampleAnomalyDetectionActions.`learn(Network networkCopy, EvidenceReaderCommand evidenceReaderCommand)`	A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.

Constructors in com.bayesserver.analysis with parameters of type Network
Constructor	Description
`SensitivityToParameters(Network network, InferenceFactory factory)`	Initializes a new instance of the `SensitivityToParameters` class .

Uses of Network in com.bayesserver.causal

Methods in com.bayesserver.causal that return Network
Modifier and Type	Method	Description
`Network`	BackdoorCriterion.`getNetwork()`	The Bayesian network on which the identification is based.
`Network`	CausalInferenceBase.`getNetwork()`	The target Bayesian network.
`Network`	DisjunctiveCauseCriterion.`getNetwork()`	The Bayesian network on which the identification is based.
`Network`	FrontDoorCriterion.`getNetwork()`	The Bayesian network on which the identification is based.
`Network`	Identification.`getNetwork()`	The Bayesian network on which the identification is based.

Methods in com.bayesserver.causal with parameters of type Network
Modifier and Type	Method	Description
`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`	BackdoorGraph.`convert(Network network, List<CausalNode> treatments, List<CausalNode> outcomes, 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).
`static void`	IndirectGraph.`convert(Network network, List<CausalNode> treatments, List<CausalNode> outcomes, IndirectGraphOptions options)`	Constructs the 'Indirect graph' from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
`Inference`	BackdoorInferenceFactory.`createInferenceEngine(Network network)`	Creates an instance of an inference algorithm, with the [network] as it's target.
`Inference`	DisjunctiveCauseInferenceFactory.`createInferenceEngine(Network network)`	Creates an instance of an inference algorithm, with the [network] as it's target.
`Inference`	FrontDoorInferenceFactory.`createInferenceEngine(Network network)`	Creates an instance of an inference algorithm, with the [network] as it's target.
`protected void`	CausalInferenceBase.`setNetwork(Network value)`

Constructors in com.bayesserver.causal with parameters of type Network
Constructor	Description
`BackdoorCriterion(Network network)`	Initializes a new instance of the `BackdoorCriterion` class.
`BackdoorInference(Network network)`	Initializes a new instance of the `BackdoorInference` class.
`CausalInferenceBase(Network network, InferenceFactory factory)`	Initializes a new instance of the `CausalInferenceBase` class.
`DisjunctiveCauseCriterion(Network network)`	Initializes a new instance of the `DisjunctiveCauseCriterion` class.
`DisjunctiveCauseInference(Network network)`	Initializes a new instance of the `DisjunctiveCauseInference` class.
`FrontDoorCriterion(Network network)`	Initializes a new instance of the `FrontDoorCriterion` class.
`FrontDoorInference(Network network)`	Initializes a new instance of the `FrontDoorInference` class.

Uses of Network in com.bayesserver.data

Methods in com.bayesserver.data that return Network
Modifier and Type	Method	Description
`Network`	CrossValidationNetwork.`getNetwork()`	Gets the network learnt from the cross validation partitioning.
`Network`	DefaultCrossValidationNetwork.`getNetwork()`	Gets the network learnt from a cross validation partitioning.

Methods in com.bayesserver.data with parameters of type Network
Modifier and Type	Method	Description
`EvidenceReaderCommand`	DataTableEvidenceReaderCommandFactory.`create(Network network)`	Create an evidence reader command, based on a specific network which may be a copy of the original.
`EvidenceReaderCommand`	EvidenceReaderCommandFactory.`create(Network network)`	Create an evidence reader command, based on a specific network which may be a copy of the original.
`EvidenceReaderCommand`	DataTableEvidenceReaderCommandFactory.`createPartitioned(Network network, DataPartitioning dataPartitioning, int partitionCount)`	Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
`EvidenceReaderCommand`	EvidenceReaderCommandFactory.`createPartitioned(Network network, DataPartitioning dataPartitioning, int partitionCount)`	Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
`void`	DefaultCrossValidationNetwork.`setNetwork(Network value)`	Sets the network learnt from a cross validation partitioning.

Constructors in com.bayesserver.data with parameters of type Network
Constructor	Description
`DefaultCrossValidationNetwork(Network network)`	Initializes a new instance of the `DefaultCrossValidationNetwork` class, with a network.

Uses of Network in com.bayesserver.data.sampling

Methods in com.bayesserver.data.sampling that return Network
Modifier and Type	Method	Description
`Network`	DataSampler.`getNetwork()`	Gets the Bayesian network or Dynamic Bayesian network that was used in the constructor.

Constructors in com.bayesserver.data.sampling with parameters of type Network
Constructor	Description
`DataSampler(Network network)`	Initializes a new instance of the `DataSampler` class.
`DataSampler(Network network, Evidence fixedData)`	Initializes a new instance of the `DataSampler` class.

Uses of Network in com.bayesserver.inference

Methods in com.bayesserver.inference that return Network
Modifier and Type	Method	Description
`Network`	DefaultEvidence.`getNetwork()`	Gets the Bayesian network that is the the target of the evidence.
`Network`	DefaultQueryDistributionCollection.`getNetwork()`	Gets the `Network` that is the target for a `Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput)`.
`Network`	DefaultQueryFunctionCollection.`getNetwork()`	Gets the `Network` that is the target for a `Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput)`.
`Network`	Evidence.`getNetwork()`	Gets the Bayesian network that is the the target of the evidence.
`Network`	Inference.`getNetwork()`	The target Bayesian network.
`Network`	LikelihoodSamplingInference.`getNetwork()`	The target Bayesian network.
`Network`	LoopyBeliefInference.`getNetwork()`	The target Bayesian network.
`Network`	RelevanceTreeInference.`getNetwork()`	The target Bayesian network.
`Network`	VariableEliminationInference.`getNetwork()`	The target Bayesian network.

Methods in com.bayesserver.inference with parameters of type Network
Modifier and Type	Method	Description
`Inference`	InferenceFactory.`createInferenceEngine(Network network)`	Creates an instance of an inference algorithm, with the [network] as it's target.
`Inference`	LikelihoodSamplingInferenceFactory.`createInferenceEngine(Network network)`	Creates an instance of an inference algorithm, with the [network] as it's target.
`Inference`	LoopyBeliefInferenceFactory.`createInferenceEngine(Network network)`	Creates an instance of an inference algorithm, with the [network] as it's target.
`Inference`	RelevanceTreeInferenceFactory.`createInferenceEngine(Network network)`	Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
`Inference`	VariableEliminationInferenceFactory.`createInferenceEngine(Network network)`	Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
`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.

Constructors in com.bayesserver.inference with parameters of type Network
Constructor	Description
`DefaultEvidence(Network network)`	Initializes a new instance of the `DefaultEvidence` class, with the target Bayesian network.
`DefaultQueryDistributionCollection(Network network)`	Initializes a new instance of the `DefaultQueryDistributionCollection` class, passing the target Bayesian network as a parameter.
`DefaultQueryFunctionCollection(Network network)`	Initializes a new instance of the `DefaultQueryFunctionCollection` class, passing the target Bayesian network as a parameter.
`LikelihoodSamplingInference(Network network)`	Initializes a new instance of the `LikelihoodSamplingInference` class, with the target Bayesian network.
`LoopyBeliefInference(Network network)`	Initializes a new instance of the `LoopyBeliefInference` class, with the target Bayesian network.
`RelevanceTreeInference(Network network)`	Initializes a new instance of the `RelevanceTreeInference` class, with the target Bayesian network.
`VariableEliminationInference(Network network)`	Initializes a new instance of the `VariableEliminationInference` class, with the target Bayesian network.

Uses of Network in com.bayesserver.learning.parameters

Methods in com.bayesserver.learning.parameters that return Network
Modifier and Type	Method	Description
`Network`	DistributedMapperContext.`getNetwork()`	Gets the `Network` that is being learnt by the distributed process.
`Network`	ParameterLearning.`getNetwork()`	Returns the relevant network.

Methods in com.bayesserver.learning.parameters with parameters of type Network
Modifier and Type	Method	Description
`static ParameterLearningOutput`	ParameterLearning.`learnDistributed(Network network, ParameterLearningOptions options, Distributer<DistributerContext> distributer)`	Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
`static ParameterLearningOutput`	ParameterLearning.`learnDistributed(Network network, List<DistributionSpecification> distributionSpecifications, ParameterLearningOptions options, Distributer<DistributerContext> distributer)`	Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.

Constructors in com.bayesserver.learning.parameters with parameters of type Network
Constructor	Description
`OnlineLearning(Network network, InferenceFactory factory)`	Initializes a new instance of the `OnlineLearning` class.
`ParameterLearning(Network network, InferenceFactory factory)`	Initializes a new instance of the `ParameterLearning` class.

Uses of Network in com.bayesserver.optimization

Methods in com.bayesserver.optimization with parameters of type Network
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).