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

Packages that use Variable
Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.causal
com.bayesserver.data
com.bayesserver.data.discovery
com.bayesserver.data.sampling
com.bayesserver.inference
com.bayesserver.learning.structure
com.bayesserver.optimization

Uses of Variable in com.bayesserver

Methods in com.bayesserver that return Variable
Modifier and Type	Method	Description
`Variable`	Variable.`copy()`	Copies this instance.
`Variable`	NetworkVariableCollection.`get(int index)`	Gets the `Variable` object at the specified index.
`Variable`	NetworkVariableCollection.`get(String name)`	Performs a case sensitive lookup.
`Variable`	NetworkVariableCollection.`get(String name, boolean throwIfNotFound)`	Performs a case sensitive lookup.
`Variable`	NodeVariableCollection.`get(int index)`	Gets the `Variable` object at the specified index.
`Variable`	NodeVariableCollection.`get(String name)`	Performs a case sensitive lookup.
`Variable`	NodeVariableCollection.`get(String name, boolean throwIfNotFound)`	Performs a case sensitive lookup.
`Variable`	DecomposeOutput.`getDecomposedVariable(Variable networkVariable)`	Maps a variable in the original network to the equivalent variable in the decomposed network.
`Variable`	DecomposeOutput.`getOriginalVariable(Variable decomposedVariable)`	Maps a variable in the decomposed network to the equivalent variable in the original network.
`Variable`	FunctionVariableExpression.`getOwner()`	Gets the current owner, if assigned to a variable.
`Variable`	QueryExpression.`getOwner()`	Gets the current owner, if assigned to a variable.
`Variable`	UnrollOutput.`getUnrolledVariable(Variable dbnVariable, Integer time)`	Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
`Variable`	State.`getVariable()`	Gets the `Variable` the state belongs to, if any.
`Variable`	StateCollection.`getVariable()`	Gets the `Variable` this collection belongs to.
`Variable`	UnrollOutput.VariableTime.`getVariable()`	Gets the variable.
`Variable`	VariableContext.`getVariable()`	Gets the variable.
`Variable`	NodeVariableCollection.`remove(int index)`	Removes an element from the collection at the specified index.
`Variable`	NetworkVariableCollection.`set(int index, Variable value)`	Gets the `Variable` object at the specified index.
`Variable`	NodeVariableCollection.`set(int index, Variable value)`	Sets the `Variable` object at the specified index.

Methods in com.bayesserver with parameters of type Variable
Modifier and Type	Method	Description
`void`	NodeVariableCollection.`add(int index, Variable item)`	Inserts an element into the collection at the specified index.
`int`	Variable.`compareTo(Variable other)`
`boolean`	VariableContextCollection.`contains(Variable variable)`	Determines whether a `Variable` is in the collection.
`boolean`	VariableContextCollection.`contains(Variable variable, Integer time)`	Determines whether a `Variable` is in the collection at the specified [time].
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Variable continuousHeadB)`	Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Variable continuousHeadB, State... discrete)`	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Variable continuousHeadB, StateContext... discrete)`	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Variable continuousHeadB, TableIterator iterator)`	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB)`	Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, State... discrete)`	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, StateContext... discrete)`	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`double`	CLGaussian.`getCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, TableIterator iterator)`	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`UnrollOutput.VariableTime`	UnrollOutput.`getDbnVariable(Variable unrolledVariable)`	Maps from a variable in the unrolled network to the corresponding variable in the original Dynamic Bayesian network.
`Variable`	DecomposeOutput.`getDecomposedVariable(Variable networkVariable)`	Maps a variable in the original network to the equivalent variable in the decomposed network.
`double`	CLGaussian.`getMean(Variable continuousHead)`	Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`double`	CLGaussian.`getMean(Variable continuousHead, State... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	CLGaussian.`getMean(Variable continuousHead, StateContext... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	CLGaussian.`getMean(Variable continuousHead, TableIterator iterator)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`double`	CLGaussian.`getMean(Variable continuousHead, Integer time)`	Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
`double`	CLGaussian.`getMean(Variable continuousHead, Integer time, State... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	CLGaussian.`getMean(Variable continuousHead, Integer time, StateContext... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	CLGaussian.`getMean(Variable continuousHead, Integer time, TableIterator iterator)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`Variable`	DecomposeOutput.`getOriginalVariable(Variable decomposedVariable)`	Maps a variable in the decomposed network to the equivalent variable in the original network.
`Variable`	UnrollOutput.`getUnrolledVariable(Variable dbnVariable, Integer time)`	Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
`double`	CLGaussian.`getVariance(Variable continuousHead)`	Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`double`	CLGaussian.`getVariance(Variable continuousHead, State... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	CLGaussian.`getVariance(Variable continuousHead, StateContext... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	CLGaussian.`getVariance(Variable continuousHead, TableIterator iterator)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	CLGaussian.`getVariance(Variable continuousHead, Integer time)`	Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`double`	CLGaussian.`getVariance(Variable continuousHead, Integer time, State... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	CLGaussian.`getVariance(Variable continuousHead, Integer time, StateContext... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	CLGaussian.`getVariance(Variable continuousHead, Integer time, TableIterator iterator)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	CLGaussian.`getWeight(Variable continuousHead, Variable continuousTail)`	Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
`double`	CLGaussian.`getWeight(Variable continuousHead, Variable continuousTail, State... discrete)`	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`double`	CLGaussian.`getWeight(Variable continuousHead, Variable continuousTail, StateContext... discrete)`	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`double`	CLGaussian.`getWeight(Variable continuousHead, Variable continuousTail, TableIterator iterator)`	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`double`	CLGaussian.`getWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail)`	Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
`double`	CLGaussian.`getWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, State... discrete)`	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`double`	CLGaussian.`getWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, StateContext... discrete)`	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`double`	CLGaussian.`getWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, TableIterator iterator)`	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`int`	VariableContextCollection.`indexOf(Variable item)`	Determines the index of a specific `Variable` in the collection.
`int`	VariableContextCollection.`indexOf(Variable variable, Integer time)`	Determines the index of a specific `Variable` in the collection at the specified [time].
`CLGaussian`	CLGaussian.`instantiate(Variable variable, double value)`	Calculates the distribution which results from instantiating a particular variable.
`CLGaussian`	CLGaussian.`instantiate(Variable variable, double value, Integer time)`	Calculates the distribution which results from instantiating a particular variable at a specified time.
`CLGaussian`	CLGaussian.`instantiateHead(Variable variable, double value, Integer time)`	Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
`CLGaussian`	CLGaussian.`instantiateHead(Variable variable, double value, Integer time, double[] logPdf)`	Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
`boolean`	NodeVariableCollection.`remove(Variable item)`	Removes the `Variable` from the collection.
`Variable`	NetworkVariableCollection.`set(int index, Variable value)`	Gets the `Variable` object at the specified index.
`Variable`	NodeVariableCollection.`set(int index, Variable value)`	Sets the `Variable` object at the specified index.
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Variable continuousHeadB, double value)`	Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Variable continuousHeadB, double value, State... discrete)`	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Variable continuousHeadB, double value, StateContext... discrete)`	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Variable continuousHeadB, double value, TableIterator iterator)`	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, double value)`	Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, double value, State... discrete)`	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, double value, StateContext... discrete)`	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`void`	CLGaussian.`setCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, double value, TableIterator iterator)`	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
`void`	CLGaussian.`setMean(Variable continuousHead, double value)`	Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	CLGaussian.`setMean(Variable continuousHead, double value, State... discrete)`	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`void`	CLGaussian.`setMean(Variable continuousHead, double value, StateContext... discrete)`	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`void`	CLGaussian.`setMean(Variable continuousHead, double value, TableIterator iterator)`	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`void`	CLGaussian.`setMean(Variable continuousHead, Integer time, double value)`	Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	CLGaussian.`setMean(Variable continuousHead, Integer time, double value, State... discrete)`	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`void`	CLGaussian.`setMean(Variable continuousHead, Integer time, double value, StateContext... discrete)`	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`void`	CLGaussian.`setMean(Variable continuousHead, Integer time, double value, TableIterator iterator)`	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`void`	CLGaussian.`setVariance(Variable continuousHead, double value)`	Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	CLGaussian.`setVariance(Variable continuousHead, double value, State... discrete)`	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`void`	CLGaussian.`setVariance(Variable continuousHead, double value, StateContext... discrete)`	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`void`	CLGaussian.`setVariance(Variable continuousHead, double value, TableIterator iterator)`	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`void`	CLGaussian.`setVariance(Variable continuousHead, Integer time, double value)`	Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	CLGaussian.`setVariance(Variable continuousHead, Integer time, double value, State... discrete)`	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`void`	CLGaussian.`setVariance(Variable continuousHead, Integer time, double value, StateContext... discrete)`	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`void`	CLGaussian.`setVariance(Variable continuousHead, Integer time, double value, TableIterator iterator)`	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`void`	CLGaussian.`setWeight(Variable continuousHead, Variable continuousTail, double value)`	Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
`void`	CLGaussian.`setWeight(Variable continuousHead, Variable continuousTail, double value, State... discrete)`	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`void`	CLGaussian.`setWeight(Variable continuousHead, Variable continuousTail, double value, StateContext... discrete)`	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`void`	CLGaussian.`setWeight(Variable continuousHead, Variable continuousTail, double value, TableIterator iterator)`	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`void`	CLGaussian.`setWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, double value)`	Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
`void`	CLGaussian.`setWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, double value, State... discrete)`	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`void`	CLGaussian.`setWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, double value, StateContext... discrete)`	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`void`	CLGaussian.`setWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, double value, TableIterator iterator)`	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
`void`	NetworkMonitor.`statesCollectionChange(Variable variable, int index, State add, State remove, CollectionAction action, boolean complete)`	For internal use.
`void`	NetworkMonitor.`variableCollectionChange(int index, Variable add, Variable remove, CollectionAction action, boolean complete)`	For internal use.

Method parameters in com.bayesserver with type arguments of type Variable
Modifier and Type	Method	Description
`boolean`	VariableContextCollection.`containsAll(List<Variable> items)`	Determines whether all [items] are matched in the collection.
`boolean`	VariableContextCollection.`containsAll(List<Variable> items, List<Integer> times)`	Determines whether all [items] are matched in the collection.
`boolean`	VariableContextCollection.`containsAny(List<Variable> items, List<Integer> times)`	Determines whether any [items] are matched in the collection.

Constructors in com.bayesserver with parameters of type Variable
Constructor	Description
`CLGaussian(Variable variable)`	Initializes a new instance of the `CLGaussian` class with a single variable.
`CLGaussian(Variable[] variables)`	Initializes a new instance of the `CLGaussian` class with the specified variables.
`CLGaussian(Variable variable, Integer time)`	Initializes a new instance of the `CLGaussian` class with a single variable at the specified time.
`Node(Variable variable)`	Initializes a new instance of the `Node` class with a specified `Variable` and assigns the name of the variable to the node.
`Node(String name, Variable... variables)`	Initializes a new instance of the `Node` class with a specified name and a number of variables.
`Table(Variable variable)`	Initializes a new instance of the `Table` class with a single `Variable`.
`Table(Variable... variables)`	Initializes a new instance of the `Table` class with the specified variables.
`Table(Variable variable, Integer time)`	Initializes a new instance of the `Table` class with a single `Variable` and time.
`TableAccessor(Table table, Variable[] order)`	Initializes a new instance of the `TableAccessor` class, allowing random access to [table] with a specified [order] for the variables.
`TableAccessor(Table table, Variable[] order, Integer[] times)`	Initializes a new instance of the `TableAccessor` class, allowing random access to [table] with a specified [order] for the variables at specified times.
`TableIterator(Table table, Variable[] order)`	Initializes a new instance of the `TableIterator` class, allowing sequential access to [table] with a specified [order] for the variables.
`TableIterator(Table table, Variable[] order, Integer[] times)`	Initializes a new instance of the `TableIterator` class, allowing sequential access to [table] with a specified [order] for the variables at specified times.
`VariableContext(Variable variable)`	Initializes a new instance of the `VariableContext` class.
`VariableContext(Variable variable, HeadTail headTail)`	Initializes a new instance of the `VariableContext` class.
`VariableContext(Variable variable, Integer time)`	Initializes a new instance of the `VariableContext` class.
`VariableContext(Variable variable, Integer time, HeadTail headTail)`	Initializes a new instance of the `VariableContext` class.

Constructor parameters in com.bayesserver with type arguments of type Variable
Constructor	Description
`CLGaussian(List<Variable> variables, Integer time)`	Initializes a new instance of the `CLGaussian` class with the specified variables at a particular time.
`CLGaussian(List<Variable> variables, Integer time, HeadTail headTail)`	Initializes a new instance of the `CLGaussian` class with the specified variables.
`Node(String name, List<Variable> variables)`	Initializes a new instance of the `Node` class with a specified name and a number of variables.
`Table(List<Variable> variables, Integer time)`	Initializes a new instance of the `Table` class with the specified variables, at an optional time.
`Table(List<Variable> variables, Integer time, HeadTail headTail)`	Initializes a new instance of the `Table` class with the specified variables, at an optional time.
`TableAccessor(Table table, List<Variable> order, List<Integer> times)`	Initializes a new instance of the `TableAccessor` class, allowing random access to [table] with a specified [order] for the variables at specified times.
`TableIterator(Table table, List<Variable> order, List<Integer> times)`	Initializes a new instance of the `TableIterator` class, allowing sequential access to [table] with a specified [order] for the variables at specified times.
`VariableMap(VariableContextCollection sortedVariables, List<Variable> order, List<Integer> times)`	Initializes a new instance of the `VariableMap` class.

Uses of Variable in com.bayesserver.analysis

Methods in com.bayesserver.analysis that return Variable
Modifier and Type	Method	Description
`Variable`	AutoInsightVariableOutput.`getVariable()`	Gets the test variable.

Methods in com.bayesserver.analysis with parameters of type Variable
Modifier and Type	Method	Description
`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 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 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 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.

Method parameters in com.bayesserver.analysis with type arguments of type Variable
Modifier and Type	Method	Description
`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)`	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 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(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.
`static void`	Combinations.`enumerate(List<Variable> variables, CombinationAction combinationAction, CombinationOptions options)`	Enumerates the state combinations for a set of variables.

Constructors in com.bayesserver.analysis with parameters of type Variable
Constructor	Description
`AssociationPair(Variable x, Variable y)`	Initializes a new instance of the `AssociationPair` class with individual variables.

Uses of Variable in com.bayesserver.causal

Methods in com.bayesserver.causal that return Variable
Modifier and Type	Method	Description
`Variable`	EffectsAnalysisOutput.`getOutcome()`	Gets the outome (target) variable on which effects are being measured.
`Variable`	EffectsAnalysisOutput.`getTreatment()`	Gets the treatment variable which is being varied.
`Variable`	EffectsAnalysisOutputItem.`getTreatmentVariable()`	Gets the treatment variable used to measure the causal effect on the treatment.

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

Method parameters in com.bayesserver.causal with type arguments of type Variable
Modifier and Type	Method	Description
`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'.

Uses of Variable in com.bayesserver.data

Methods in com.bayesserver.data that return Variable
Modifier and Type	Method	Description
`Variable`	VariableReference.`getVariable()`	Gets the variable.

Methods in com.bayesserver.data with parameters of type Variable
Modifier and Type	Method	Description
`VariableReference`	VariableReference.`copy(Variable newVariable)`	Creates a copy of this instance, but based on a different variable.

Constructors in com.bayesserver.data with parameters of type Variable
Constructor	Description
`VariableReference(Variable variable, ColumnValueType columnValueType, String column)`	Initializes a new instance of the `VariableReference` class.
`VariableReference(Variable variable, ColumnValueType columnValueType, String column, StateNotFoundAction stateNotFoundAction)`	Initializes a new instance of the `VariableReference` class.
`VariableReference(Variable variable, ColumnValueType columnValueType, String column, StateNotFoundAction stateNotFoundAction, EmptyStringAction emptyStringAction)`	Initializes a new instance of the `VariableReference` class.
`VariableReference(Variable variable, ColumnValueType columnValueType, String column, StateNotFoundAction stateNotFoundAction, EmptyStringAction emptyStringAction, String interventionColumn)`	Initializes a new instance of the `VariableReference` class.

Uses of Variable in com.bayesserver.data.discovery

Methods in com.bayesserver.data.discovery that return Variable
Modifier and Type Method Description

Variable VariableInfo.getVariable()
Gets the generated Variable.

Methods in com.bayesserver.data.discovery that return Variable
Modifier and Type	Method	Description
`Variable`	VariableInfo.`getVariable()`	Gets the generated `Variable`.

Uses of Variable in com.bayesserver.data.sampling

Methods in com.bayesserver.data.sampling that return Variable
Modifier and Type	Method	Description
`Variable`	ExcludedVariables.`get(int index)`
`Variable`	ExcludedVariables.`remove(int index)`
`Variable`	ExcludedVariables.`set(int index, Variable item)`

Methods in com.bayesserver.data.sampling with parameters of type Variable
Modifier and Type	Method	Description
`void`	ExcludedVariables.`add(int index, Variable element)`
`Variable`	ExcludedVariables.`set(int index, Variable item)`

Uses of Variable in com.bayesserver.inference

Methods in com.bayesserver.inference that return Variable
Modifier and Type	Method	Description
`Variable`	QueryFunctionOutput.`getVariable()`	The function variable to evaluate.

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

Constructors in com.bayesserver.inference with parameters of type Variable
Constructor	Description
`QueryFunctionOutput(Variable variable)`	Initializes a new instance of the `com.bayesserver.QueryFunctionOutput` class.

Uses of Variable in com.bayesserver.learning.structure

Methods in com.bayesserver.learning.structure that return Variable
Modifier and Type	Method	Description
`Variable`	FeatureSelectionTest.`getTarget()`	Gets the variable that was the target of the feature selection test.
`Variable`	FeatureSelectionTest.`getVariable()`	Gets the variable which was tested to see if it is likely to be a feature of the `FeatureSelectionTest.getTarget()` variable.

Methods in com.bayesserver.learning.structure with parameters of type Variable
Modifier and Type	Method	Description
`static FeatureSelectionOutput`	FeatureSelection.`detect(List<Variable> variables, EvidenceReaderCommand evidenceReaderCommand, Variable target, FeatureSelectionOptions options)`	Determines which variables are likely to be good features (predictors) of a target variable.

Method parameters in com.bayesserver.learning.structure with type arguments of type Variable
Modifier and Type	Method	Description
`static FeatureSelectionOutput`	FeatureSelection.`detect(List<Variable> variables, EvidenceReaderCommand evidenceReaderCommand, Variable target, FeatureSelectionOptions options)`	Determines which variables are likely to be good features (predictors) of a target variable.

Uses of Variable in com.bayesserver.optimization

Methods in com.bayesserver.optimization that return Variable
Modifier and Type	Method	Description
`Variable`	DesignVariable.`getVariable()`	Gets the variable these options refer to.
`Variable`	Objective.`getVariable()`	Gets the variable being optimized.

Constructors in com.bayesserver.optimization with parameters of type Variable
Constructor	Description
`DesignVariable(Variable variable, Double lowerBound, Double upperBound, boolean allowMissing)`	Initializes a new instance of the `com.bayesserver.optization.DesignVariable` class, automatically generating the necessary design states.
`DesignVariable(Variable variable, Double lowerBound, Double upperBound, boolean allowMissing, InterventionType interventionType)`	Initializes a new instance of the `DesignVariable` class, automatically generating the necessary design states.
`DesignVariable(Variable variable, List<DesignState> designStates, boolean allowMissing)`	Initializes a new instance of the `DesignVariable` class.
`DesignVariable(Variable variable, List<DesignState> designStates, DesignEvidenceKind evidenceKind, boolean allowMissing, InterventionType interventionType)`	Initializes a new instance of the `DesignVariable` class.
`Objective(Variable variable, ObjectiveKind kind)`	Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.
`Objective(Variable variable, ObjectiveKind kind, Double value)`	Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.

Uses of Classcom.bayesserver.Variable

Uses of Variable in com.bayesserver

Uses of Variable in com.bayesserver.analysis

Uses of Variable in com.bayesserver.causal

Uses of Variable in com.bayesserver.data

Uses of Variable in com.bayesserver.data.discovery

Uses of Variable in com.bayesserver.data.sampling

Uses of Variable in com.bayesserver.inference

Uses of Variable in com.bayesserver.learning.structure

Uses of Variable in com.bayesserver.optimization

Uses of Class
com.bayesserver.Variable