Uses of Class com.bayesserver.Node (bayesserver 10.9 API)

Packages that use Node
Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.causal
com.bayesserver.inference
com.bayesserver.learning.parameters
com.bayesserver.learning.structure

Uses of Node in com.bayesserver

Methods in com.bayesserver that return Node
Modifier and Type	Method and Description
`Node`	Node.`copy()` Makes a copy of this instance.
`Node`	NetworkNodeCollection.`get(int index)` Gets the `Node` object at the specified index.
`Node`	NetworkNodeCollection.`get(String name)` Performs a case sensitive lookup.
`Node`	NetworkNodeCollection.`get(String name, boolean throwIfNotFound)` Performs a case sensitive lookup.
`Node`	Link.`getFrom()` The parent node of the directed link.
`Node`	NodeDistributionExpressions.`getNode()` Gets the node that this instance belongs to.
`Node`	NodeDistributionOptions.`getNode()` The node this instance belongs to.
`Node`	NodeDistributions.`getNode()` Gets the node that this instance belongs to.
`Node`	NodeGroupCollection.`getNode()` The `Node` the collection belongs to.
`Node`	NodeLinkCollection.`getNode()` Gets the `Node` to which the collection belongs to.
`Node`	NodeVariableCollection.`getNode()` The `Node` the collection belongs to.
`Node`	TopologicalSortNodeInfo.`getNode()` Gets the node in the network.
`Node`	UnrollOutput.NodeTime.`getNode()` Gets the node.
`Node`	Variable.`getNode()` Gets the `Node` this instance belongs to, if any.
`Node`	CLGaussian.`getOwner()` Gets the current owner, if assigned to a node.
`Node`	Distribution.`getOwner()` Gets the current owner, if assigned to a node.
`Node`	DistributionExpression.`getOwner()` Gets the current owner, if assigned to a node.
`Node`	Table.`getOwner()` Gets the current owner, if assigned to a node.
`Node`	TableExpression.`getOwner()` Gets the current owner, if assigned to a node.
`Node`	NodeDistributionKey.`getRelatedNode()` Gets the parent of the noisy node this distribution refers to, or the noisy node itself to identify the leak distribution.
`Node`	Link.`getTo()` The child node of the directed link.
`Node`	UnrollOutput.`getUnrolledNode(Node dbnNode, Integer time)` Maps between a node in the original Dynamic Bayesian network, and the corresponding node in the unrolled network.
`Node`	NetworkNodeCollection.`remove(int index)` Removes an element from the collection at the specified index, and any links that it has.
`Node`	NetworkNodeCollection.`set(int index, Node value)` Sets the `Node` object at the specified index.
`static Node[]`	TopologicalSort.`sort(Network network)` Returns the nodes in a Bayesian network sorted in topological order.

Methods in com.bayesserver with parameters of type Node
Modifier and Type	Method and Description
`void`	NetworkNodeCollection.`add(int index, Node item)` Inserts an element into the collection at the specified index.
`void`	NetworkMonitor.`causalObservabilityChanged(Node node, CausalObservability newCausalObservability, CausalObservability oldCausalObservability)` For internal use.
`Link`	Link.`copy(Node from, Node to, int temporalOrder)` Creates a new link, copying the properties from this instance, such as `Link.getDescription()` and `Link.getCustomProperties()`.
`void`	NetworkMonitor.`distributionChanged(Node node, NodeDistributionKey key, NodeDistributionKind kind, Distribution newDistribution, Distribution oldDistribution)` For internal use.
`Link`	NetworkLinkCollection.`find(Node from, Node to)` Finds a link from one node to another if it exists, otherwise returns null.
`Link`	NetworkLinkCollection.`find(Node from, Node to, int temporalOrder)` Finds a link from one node to another if it exists, otherwise returns null.
`UnrollOutput.NodeTime`	UnrollOutput.`getDbnNode(Node unrolledNode)` Maps from a node in the unrolled network to the corresponding node in the original Dynamic Bayesian network.
`static double`	ParameterCounter.`getParameterCount(Node node, int order)` Gets the parameter count for an individual node distribution.
`static double`	ParameterCounter.`getParameterCount(Node node, NodeDistributionKey key)` Gets the parameter count for an individual node distribution.
`Node`	UnrollOutput.`getUnrolledNode(Node dbnNode, Integer time)` Maps between a node in the original Dynamic Bayesian network, and the corresponding node in the unrolled network.
`void`	NetworkMonitor.`nodeCollectionChange(int index, Node add, Node remove, CollectionAction action, boolean complete)` For internal use.
`void`	NetworkMonitor.`noisyNodeTypeChanged(Node node, NoisyType newNoisyType, NoisyType oldNoisyType)` For internal use.
`boolean`	NetworkNodeCollection.`remove(Node item)` Removes the `Node` from the collection.
`Node`	NetworkNodeCollection.`set(int index, Node value)` Sets the `Node` object at the specified index.

Constructors in com.bayesserver with parameters of type Node
Constructor and Description
`CLGaussian(Node node)` Initializes a new instance of the `CLGaussian` class with the variables of a single node.
`CLGaussian(Node node, Integer time)` Initializes a new instance of the `CLGaussian` class with the variables of a single node at the specified time.
`Link(Node from, Node to)` Initializes a new instance of the `Link` class with the parent node specified in [from] and the child in [to].
`Link(Node from, Node to, int temporalOrder)` Initializes a new instance of the `Link` class with a specified [temporalOrder], the parent node specified in [from] and the child in [to].
`NodeDistributionKey(int order, Node relatedNode)` Initializes a new instance of a `NodeDistributionKey`.
`NodeDistributionKey(Node relatedNode)` Initializes a new instance of a `NodeDistributionKey`.
`Table(Node... nodes)` Initializes a new instance of the `Table` class with all the variables from the supplied nodes.
`Table(Node node)` Initializes a new instance of the `Table` class with the specified node variables.
`Table(Node[] nodes, HeadTail headTail)` Initializes a new instance of the `Table` class with all the variables from the supplied nodes.
`Table(Node node, Integer time)` Initializes a new instance of the `Table` class with the specified node variable at the specified time.
`TableAccessor(Table table, Node[] order)` Initializes a new instance of the `TableAccessor` class, allowing random access to [table] with a specified [order] for the node variables.
`TableAccessor(Table table, Node[] order, Integer[] times)` Initializes a new instance of the `TableAccessor` class, allowing random access to [table] with a specified [order] for the node variables.
`TableIterator(Table table, Node[] order)` Initializes a new instance of the `TableIterator` class, allowing sequential access to [table] with a specified [order] for the node variables.
`TableIterator(Table table, Node[] order, Integer[] times)` Initializes a new instance of the `TableIterator` class, allowing sequential access to [table] with a specified [order] for the node variables.
`VariableMap(VariableContextCollection sortedVariables, Node[] order)` Initializes a new instance of the `VariableMap` class.

Uses of Node in com.bayesserver.analysis

Methods in com.bayesserver.analysis that return Node
Modifier and Type	Method and Description
`Node`	DSeparationTestResult.`getNode()` The test node.
`Node`	ParameterReference.`getNode()` Gets the node whose distribution parameter is being referenced.

Method parameters in com.bayesserver.analysis with type arguments of type Node
Modifier and Type	Method and Description
`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 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 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<Node> testNodes, Evidence evidence, DSeparationOptions options)` Calculates whether sets of nodes are D-Separated, given any evidence.

Constructors in com.bayesserver.analysis with parameters of type Node
Constructor and Description
`AssociationPair(Node x, Node y)` Initializes a new instance of the `AssociationPair` class with individual nodes.
`ParameterReference(Node node, NodeDistributionKey key, State[] states)` Initializes a new instance of the `ParameterReference` class .
`ParameterReference(Node node, State[] states)` Initializes a new instance of the `ParameterReference` class.

Uses of Node in com.bayesserver.causal

Methods in com.bayesserver.causal that return Node
Modifier and Type	Method and Description
`Node`	AdjustmentSetNode.`getNode()` Gets the node.
`Node`	CausalNode.`getNode()` Gets the Bayesian network node.
`Node`	DisjunctiveCauseSetNode.`getNode()` Gets the node.
`Node`	FrontDoorSetNode.`getNode()` Gets the node.
`Node`	NodeSetItem.`getNode()` Gets the node.

Constructors in com.bayesserver.causal with parameters of type Node
Constructor and Description
`AdjustmentSetNode(Node node)` Initializes a new instance of the `AdjustmentSetNode` class.
`AdjustmentSetNode(Node node, Integer time)` Initializes a new instance of the `AdjustmentSetNode` class.
`CausalNode(Node node)` Initializes a new instance of the `CausalNode` class.
`CausalNode(Node node, Integer time)` Initializes a new instance of the `CausalNode` class.
`DisjunctiveCauseSetNode(Node node)` Initializes a new instance of the `DisjunctiveCauseSetNode` class.
`DisjunctiveCauseSetNode(Node node, Integer time)` Initializes a new instance of the `DisjunctiveCauseSetNode` class.
`FrontDoorSetNode(Node node)` Initializes a new instance of the `FrontDoorSetNode` class.
`FrontDoorSetNode(Node node, Integer time)` Initializes a new instance of the `FrontDoorSetNode` class.

Uses of Node in com.bayesserver.inference

Methods in com.bayesserver.inference with parameters of type Node
Modifier and Type	Method and Description
`void`	DefaultEvidence.`clear(Node node)` Clears evidence on a node's variables.
`void`	Evidence.`clear(Node node)` Clears evidence on a node's variables.
`void`	DefaultEvidence.`clear(Node node, Integer time)` Clears evidence on a node's single variable.
`void`	Evidence.`clear(Node node, Integer time)` Clears evidence on a node's single variable.
`Double`	DefaultEvidence.`get(Node node)` Gets the hard evidence value for a particular node's variable, or returns null if the `EvidenceType` equals `EvidenceType.NONE` or `EvidenceType.SOFT`.
`Double`	Evidence.`get(Node node)` Gets the hard evidence value for a particular node's variable, or returns null if the `EvidenceType` equals `EvidenceType.NONE` or `EvidenceType.SOFT`.
`void`	DefaultEvidence.`get(Node node, Double[] destination, int destinationStart, int startTime, int count)` Gets the evidence for a node's single temporal variable.
`void`	Evidence.`get(Node node, Double[] destination, int destinationStart, int startTime, int count)` Gets the evidence for a node's single temporal variable.
`Double`	DefaultEvidence.`get(Node node, Integer time)` Gets the evidence for a node with a single variable at the specified time.
`Double`	Evidence.`get(Node node, Integer time)` Gets the evidence for a node with a single variable at the specified time.
`EvidenceType`	DefaultEvidence.`getEvidenceType(Node node)` Returns the type of evidence currently set for a node with a single variable.
`EvidenceType`	Evidence.`getEvidenceType(Node node)` Returns the type of evidence currently set for a node with a single variable.
`EvidenceType`	DefaultEvidence.`getEvidenceType(Node node, Integer time)` Returns the type of evidence currently set for a node with a single variable at a given time.
`EvidenceType`	Evidence.`getEvidenceType(Node node, Integer time)` Returns the type of evidence currently set for a node with a single variable at a given time.
`EvidenceTypes`	DefaultEvidence.`getEvidenceTypes(Node node)` Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
`EvidenceTypes`	Evidence.`getEvidenceTypes(Node node)` Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
`EvidenceTypes`	DefaultEvidence.`getEvidenceTypes(Node node, Integer time)` Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
`EvidenceTypes`	Evidence.`getEvidenceTypes(Node node, Integer time)` Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
`Integer`	DefaultEvidence.`getState(Node node)` Gets the hard evidence state for node with a single variable, or returns null if the `EvidenceType` equals `EvidenceType.NONE` or `EvidenceType.SOFT`.
`Integer`	Evidence.`getState(Node node)` Gets the hard evidence state for node with a single variable, or returns null if the `EvidenceType` equals `EvidenceType.NONE` or `EvidenceType.SOFT`.
`Integer`	DefaultEvidence.`getState(Node node, Integer time)` Gets the hard evidence state for node with a single variable, or returns null if the `EvidenceType` equals `EvidenceType.NONE` or `EvidenceType.SOFT`.
`Integer`	Evidence.`getState(Node node, Integer time)` Gets the hard evidence state for node with a single variable, or returns null if the `EvidenceType` equals `EvidenceType.NONE` or `EvidenceType.SOFT`.
`void`	DefaultEvidence.`getStates(Node node, double[] buffer)` Fills out a buffer containing the soft evidence for a node with a single variable.
`void`	Evidence.`getStates(Node node, double[] buffer)` Fills out a buffer containing the soft evidence for a node with a single variable.
`void`	DefaultEvidence.`getStates(Node node, double[] buffer, Integer time)` Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
`void`	Evidence.`getStates(Node node, double[] buffer, Integer time)` Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
`void`	DefaultEvidence.`set(Node node, Double value)` Sets a node's variable to a particular value (hard evidence).
`void`	Evidence.`set(Node node, Double value)` Sets a node's variable to a particular value (hard evidence).
`void`	DefaultEvidence.`set(Node node, Double[] source, int sourceStart, int startTime, int count)` Sets temporal evidence on a node with a single variable.
`void`	Evidence.`set(Node node, Double[] source, int sourceStart, int startTime, int count)` Sets temporal evidence on a node with a single variable.
`void`	DefaultEvidence.`set(Node node, Double value, Integer time)` Sets evidence on a node's single variable at a specified time.
`void`	Evidence.`set(Node node, Double value, Integer time)` Sets evidence on a node's single variable at a specified time.
`void`	DefaultEvidence.`setState(Node node, Integer state)` Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
`void`	Evidence.`setState(Node node, Integer state)` Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
`void`	DefaultEvidence.`setState(Node node, Integer state, Integer time)` Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
`void`	Evidence.`setState(Node node, Integer state, Integer time)` Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
`void`	DefaultEvidence.`setStates(Node node, double[] values)` Sets soft evidence for a discrete node with a single variable.
`void`	Evidence.`setStates(Node node, double[] values)` Sets soft evidence for a discrete node with a single variable.
`void`	DefaultEvidence.`setStates(Node node, double[] values, Integer time)` Sets soft evidence for a discrete node with a single variable, at a specified time.
`void`	Evidence.`setStates(Node node, double[] values, Integer time)` Sets soft evidence for a discrete node with a single variable, at a specified time.

Uses of Node in com.bayesserver.learning.parameters

Methods in com.bayesserver.learning.parameters that return Node
Modifier and Type	Method and Description
`Node`	DistributionSpecification.`getNode()` Gets the `Node` this distribution specification refers to.
`Node`	DistributionSpecification.`getRelatedNode()` Gets the related node (if any) of the distribution.

Methods in com.bayesserver.learning.parameters with parameters of type Node
Modifier and Type	Method and Description
`Distribution`	ParameterLearningProgressInfo.`getMonitoredDistribution(Node node)` Gets a copy of the current distribution assigned to the [node].
`Distribution`	ParameterLearningProgressInfo.`getMonitoredDistribution(Node node, Integer order)` Gets a copy of the current distribution assigned to the [node] at the requested order.
`Distribution`	ParameterLearningProgressInfo.`getMonitoredDistribution(Node node, NodeDistributionKey key)` Gets a copy of the current distribution assigned to the [node] at the requested order.

Constructors in com.bayesserver.learning.parameters with parameters of type Node
Constructor and Description
`DistributionSpecification(Node node)` Initializes a new instance of the `DistributionSpecification` class.
`DistributionSpecification(Node node, int order)` Initializes a new instance of the `DistributionSpecification` class.
`DistributionSpecification(Node node, NodeDistributionKey key)` Initializes a new instance of the `DistributionSpecification` class.

Uses of Node in com.bayesserver.learning.structure

Methods in com.bayesserver.learning.structure that return Node
Modifier and Type	Method and Description
`Node`	LinkConstraint.`getA()` Gets the first node involved in the constraint.
`Node`	LinkConstraint.`getB()` Gets the second node involved in the constraint.
`Node`	ChowLiuStructuralLearningOptions.`getRoot()` Gets the root of the Chow-Liu tree.
`Node`	TANStructuralLearningOptions.`getRoot()` Gets the root of the TAN tree.
`Node`	TANStructuralLearningOptions.`getTarget()` Gets the target of the TAN tree.

Methods in com.bayesserver.learning.structure with parameters of type Node
Modifier and Type	Method and Description
`void`	ChowLiuStructuralLearningOptions.`setRoot(Node value)` Sets the root of the Chow-Liu tree.
`void`	TANStructuralLearningOptions.`setRoot(Node value)` Sets the root of the TAN tree.
`void`	TANStructuralLearningOptions.`setTarget(Node value)` Sets the target of the TAN tree.

Method parameters in com.bayesserver.learning.structure with type arguments of type Node
Modifier and Type	Method and Description
`StructuralLearningOutput`	ChowLiuStructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	ClusteringStructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn a cluster / mixture model.
`StructuralLearningOutput`	HierarchicalStructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	PCStructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	SearchStructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	StructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	TANStructuralLearning.`learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	ChowLiuStructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	ClusteringStructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	HierarchicalStructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	PCStructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	SearchStructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	StructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.
`StructuralLearningOutput`	TANStructuralLearning.`learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)` Learn the structure (links) of a Bayesian network.

Constructors in com.bayesserver.learning.structure with parameters of type Node
Constructor and Description
`LinkConstraint(Node a, Node b, Integer temporalOrder, LinkConstraintMethod method, LinkConstraintFailureMode failureMode)` Initializes a new instance of the `LinkConstraint` class.
`LinkConstraint(Node a, Node b, LinkConstraintMethod method, LinkConstraintFailureMode failureMode)` Initializes a new instance of the `LinkConstraint` class.

Uses of Classcom.bayesserver.Node

Uses of Node in com.bayesserver

Uses of Node in com.bayesserver.analysis

Uses of Node in com.bayesserver.causal

Uses of Node in com.bayesserver.inference

Uses of Node in com.bayesserver.learning.parameters

Uses of Node in com.bayesserver.learning.structure

Uses of Class
com.bayesserver.Node