Uses of Interface
com.bayesserver.Distribution

Packages that use Distribution

Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.causal
com.bayesserver.inference
com.bayesserver.learning.parameters
com.bayesserver.statistics

Uses of Distribution in com.bayesserver

Classes in com.bayesserver that implement Distribution

Modifier and Type	Class and Description
class	CLGaussian Represents a Conditional Linear Gaussian probability distribution.
class	Table Used to represent probability distributions, conditional probability distributions, joint probability distributions and more general potentials, over a number of discrete variables.

Methods in com.bayesserver that return Distribution

Modifier and Type	Method and Description
Distribution	CLGaussian.copy() Creates a copy of the distribution.
Distribution	Distribution.copy() Creates a copy of the distribution.
Distribution	Table.copy() Creates a copy of the distribution.
Distribution	CLGaussian.copy(Integer timeShift) Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
Distribution	Distribution.copy(Integer timeShift) Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
Distribution	Table.copy(Integer timeShift) Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
Distribution	CLGaussian.divide(Distribution subset) Creates a new distribution by dividing this instance by the [subset].
Distribution	Distribution.divide(Distribution subset) Creates a new distribution by dividing the instance by the specified subset.
Distribution	Table.divide(Distribution subset) Creates a new distribution by dividing this instance by the [subset].
Distribution	NodeDistributions.findForTime(int time) Finds the temporal distribution that is suitable for the time specified.
Distribution	NodeDistributions.findForTime(int time, NodeDistributionKind kind) Finds the temporal distribution that is suitable for the time specified.
Distribution	NodeDistributions.get(int temporalOrder) Gets a distribution at a particular temporal order.
Distribution	NodeDistributions.get(NodeDistributionKey key) Gets a distribution with particular properties, such as temporal order.
Distribution	NodeDistributions.get(NodeDistributionKey key, NodeDistributionKind kind) Gets a distribution with particular properties, such as temporal order.
Distribution	NodeDistributions.get(NodeDistributionKind kind) Gets a particular kind of distribution on the node.
Distribution	Node.getDistribution() Returns the distribution currently associated with the Node.
Distribution	NodeDistributions.DistributionOrder.getDistribution() Gets the distribution.
Distribution	CLGaussian.getOuter()
Distribution	Distribution.getOuter() Returns the parent distribution, if this instance is aggregated by another distribution.
Distribution	Table.getOuter()
Distribution	CLGaussian.instantiate(Double[] values) Calculates the distribution which results from instantiating a number of variables.
Distribution	Distribution.instantiate(Double[] values) Calculates the distribution which results from instantiating a number of variables.
Distribution	Table.instantiate(Double[] values) Creates a table with a subset of variables by setting hard evidence on one or more variables.
Distribution	CLGaussian.instantiateHeads(Double[] headValues, double[] logPdf) Instantiates continuous head variable contexts.
Distribution	CLGaussian.multiply(Distribution distribution) Multiplies this instance by another distribution.
Distribution	Distribution.multiply(Distribution distribution) Creates a new distribution which is the result of multiplying this instance by another distribution.
Distribution	Table.multiply(Distribution distribution) Creates a new distribution by multiplying this instance by another distribution.
Distribution	Node.newDistribution() Creates a new distribution suitable for the node, however does not assign it to the node's Node.getDistribution() property.
Distribution	Node.newDistribution(int temporalOrder) Creates a new distribution suitable for the requested temporal order, however it is not assigned to the node.
Distribution	Node.newDistribution(NodeDistributionKey key) Creates a new distribution suitable for the requested temporal order/related node, however it is not assigned to the node.
Distribution	Node.newDistribution(NodeDistributionKey key, NodeDistributionKind kind) Creates a new distribution suitable for the requested temporal order/related node, however it is not assigned to the node.
Distribution	Node.newDistribution(NodeDistributionKey key, NodeDistributionKind kind, DistributionExpression expression) 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.
Distribution	Node.newDistribution(NodeDistributionKind kind) Creates a new distribution with the given kind, however it is not assigned to the node.

Methods in com.bayesserver that return types with arguments of type Distribution

Modifier and Type	Method and Description
Set<Map.Entry<NodeDistributionKey,Distribution>>	NodeDistributions.entrySet()

Methods in com.bayesserver with parameters of type Distribution

Modifier and Type	Method and Description
void	NetworkMonitor.distributionChanged(Node node, NodeDistributionKey key, NodeDistributionKind kind, Distribution newDistribution, Distribution oldDistribution) For internal use.
Distribution	CLGaussian.divide(Distribution subset) Creates a new distribution by dividing this instance by the [subset].
Distribution	Distribution.divide(Distribution subset) Creates a new distribution by dividing the instance by the specified subset.
Distribution	Table.divide(Distribution subset) Creates a new distribution by dividing this instance by the [subset].
void	CLGaussian.marginalize(Distribution superset) Marginalizes (integrates) the [superset] into this instance.
void	Distribution.marginalize(Distribution superset) Marginalizes (sums/integrates) the [superset] into this instance.
void	Table.marginalize(Distribution superset) Marginalizes (sums) the [superset] into this instance.
void	CLGaussian.marginalize(Distribution superset, PropagationMethod propagation) Marginalizes (integrates) the [superset] into this instance.
void	Distribution.marginalize(Distribution superset, PropagationMethod propagation) Marginalizes (sums/integrates) the [superset] into this instance.
void	Table.marginalize(Distribution superset, PropagationMethod propagation) Marginalizes (sums) the [superset] into this instance.
Distribution	CLGaussian.multiply(Distribution distribution) Multiplies this instance by another distribution.
Distribution	Distribution.multiply(Distribution distribution) Creates a new distribution which is the result of multiplying this instance by another distribution.
Distribution	Table.multiply(Distribution distribution) Creates a new distribution by multiplying this instance by another distribution.
void	NodeDistributions.set(int temporalOrder, Distribution value) Sets a distribution at a particular temporal order.
void	NodeDistributions.set(NodeDistributionKey key, Distribution value) Sets a distribution with particular properties, such as temporal order.
void	NodeDistributions.set(NodeDistributionKey key, NodeDistributionKind kind, Distribution value) Sets a distribution with particular properties, such as temporal order.
void	NodeDistributions.set(NodeDistributionKind kind, Distribution value) Sets a particular kind of distribution on the node.
void	Node.setDistribution(Distribution value) Returns the distribution currently associated with the Node.
void	NodeDistributions.validateDistribution(Distribution value, NodeDistributionKey key) Checks that a distribution is correctly specified for a particular temporal order.
void	NodeDistributions.validateDistribution(Distribution value, NodeDistributionKey key, NodeDistributionKind kind) Checks that a distribution is correctly specified for a particular temporal order.

Uses of Distribution in com.bayesserver.analysis

Methods in com.bayesserver.analysis that return Distribution

Modifier and Type	Method and Description
Distribution	AutoInsightVariableOutput.getProbabilityGivenTarget() Gets the distribution of this variable given the target.

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

Modifier and Type	Method and Description
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 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.

Uses of Distribution in com.bayesserver.causal

Methods in com.bayesserver.causal that return Distribution

Modifier and Type	Method and Description
Distribution	EffectsAnalysisOutputItem.getOutcomeDistribution() Gets P(Outcome|Do(Treatment=TreatmentState)) for discrete treatments and P(Outcome|Do(Treatment=TreatmentValue)) for continuous treatments.

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

Modifier and Type	Method and 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	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.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	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 Distribution in com.bayesserver.inference

Methods in com.bayesserver.inference that return Distribution

Modifier and Type	Method and Description
Distribution	QueryDistribution.getDistribution() Gets the distribution to query.

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

Modifier and Type	Method and Description
QueryDistribution	DefaultQueryDistributionCollection.add(Distribution distribution) Adds the specified distribution, automatically creating a QueryDistribution instance.
QueryDistribution	QueryDistributionCollection.add(Distribution distribution) Adds the specified distribution, automatically creating a QueryDistribution instance.

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

Constructor and Description
QueryDistribution(Distribution distribution) Initializes a new instance of the QueryDistribution class.
QueryDistribution(Distribution distribution, boolean isEnabled) Initializes a new instance of the QueryDistribution class.

Uses of Distribution in com.bayesserver.learning.parameters

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

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.

Uses of Distribution in com.bayesserver.statistics

Methods in com.bayesserver.statistics with parameters of type Distribution

Modifier and Type	Method and Description
static double	MutualInformation.calculate(Distribution joint, List<VariableContext> x, List<VariableContext> y, List<VariableContext> conditionOn, LogarithmBase logarithmBase) Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
static double	Entropy.calculate(Distribution joint, List<VariableContext> conditionOn, LogarithmBase logarithmBase) Measures the uncertainty of a distribution conditional on one or more variables.
static double	Entropy.calculate(Distribution joint, LogarithmBase logarithmBase) Measures the uncertainty of a distribution.
static double	MutualInformation.calculate(Distribution joint, VariableContext x, VariableContext y, List<VariableContext> conditionOn, LogarithmBase logarithmBase) Calculates mutual information or conditional mutual information, which measures the dependence between two variables.
static double	MutualInformation.calculate(Distribution joint, VariableContext x, VariableContext y, LogarithmBase logarithmBase) Measures the dependence between two variables.
static double	JensenShannon.divergence(Distribution p, Distribution q, LogarithmBase logarithm) Calculates the Jensen Shannon divergence between two distributions.
static double	KullbackLeibler.divergence(Distribution priorQ, Distribution posteriorP, LogarithmBase logarithm) Calculates the Kullback-Leibler divergence D(P||Q).