Uses of Class
com.bayesserver.VariableContext

Packages that use VariableContext

Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.inference
com.bayesserver.statistics

Uses of VariableContext in com.bayesserver

Methods in com.bayesserver that return VariableContext

Modifier and Type	Method	Description
VariableContext	VariableContextCollection.get(int index)	Gets the Variable object at the specified index.
VariableContext	VariableContextCollection.set(int index, VariableContext value)	Gets the Variable object at the specified index.

Methods in com.bayesserver with parameters of type VariableContext

Modifier and Type	Method	Description
boolean	VariableContextCollection.contains(VariableContext variableContext, boolean ignoreHeadTail)	Determines whether a variable-time (and optionally Head/Tail) combination is contained in the collection.
double	CLGaussian.getCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, State... discrete)	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
double	CLGaussian.getCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, StateContext... discrete)	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
double	CLGaussian.getCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, TableIterator iterator)	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
double	CLGaussian.getMean(VariableContext continuousHead, State... discrete)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
double	CLGaussian.getMean(VariableContext continuousHead, StateContext... discrete)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
double	CLGaussian.getMean(VariableContext continuousHead, TableIterator iterator)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
double	CLGaussian.getVariance(VariableContext continuousHead, State... discrete)	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
double	CLGaussian.getVariance(VariableContext continuousHead, StateContext... discrete)	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
double	CLGaussian.getVariance(VariableContext continuousHead, TableIterator iterator)	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
double	CLGaussian.getWeight(VariableContext continuousHead, VariableContext 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(VariableContext continuousHead, VariableContext 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(VariableContext continuousHead, VariableContext continuousTail, 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(VariableContext variableContext, boolean ignoreHeadTail)	Determines the index of a specific variable-time combination in the collection.
VariableContext	VariableContextCollection.set(int index, VariableContext value)	Gets the Variable object at the specified index.
void	CLGaussian.setCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, double value)	Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
void	CLGaussian.setCovariance(VariableContext continuousHeadA, VariableContext 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(VariableContext continuousHeadA, VariableContext 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(VariableContext continuousHeadA, VariableContext continuousHeadB, double value, TableIterator iterator)	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
void	CLGaussian.setMean(VariableContext 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(VariableContext 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(VariableContext continuousHead, double value, TableIterator iterator)	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
void	CLGaussian.setVariance(VariableContext 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(VariableContext 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(VariableContext 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.setWeight(VariableContext continuousHead, VariableContext 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(VariableContext continuousHead, VariableContext 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(VariableContext continuousHead, VariableContext 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).

Method parameters in com.bayesserver with type arguments of type VariableContext

Modifier and Type	Method	Description
boolean	VariableContextCollection.containsAll(List<VariableContext> items, boolean ignoreHeadTail)	Determines whether all [items] are matched in the collection at the specified times.

Constructors in com.bayesserver with parameters of type VariableContext

Constructor	Description
CLGaussian(VariableContext variableContext)	Initializes a new instance of the CLGaussian class from a single VariableContext.
CLGaussian(VariableContext[] variableContexts)	Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(VariableContext[] variableContexts, int count)	Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
CLGaussian(VariableContext[] variableContexts, int count, HeadTail headTail)	Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts].
Table(VariableContext variableContext)	Initializes a new instance of the Table class from a single VariableContext.
Table(VariableContext[] variableContexts)	Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(VariableContext[] buffer, int count)	Initializes a new instance of the Table class with [count] variable contexts taken from [buffer].
Table(VariableContext[] buffer, int count, HeadTail headTail)	Initializes a new instance of the Table class with [count] variable contexts taken from [buffer].
VariableContext(VariableContext variableContext)	Initializes a new instance of the VariableContext class, copying an existing instance.

Constructor parameters in com.bayesserver with type arguments of type VariableContext

Constructor	Description
CLGaussian(List<VariableContext> variableContexts)	Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts].
CLGaussian(List<VariableContext> variableContexts, HeadTail headTail)	Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts].
Table(List<VariableContext> variableContexts)	Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
Table(List<VariableContext> variableContexts, HeadTail headTail)	Initializes a new instance of the Table class with [variableContexts] specifying which variables to include in the distribution.
TableAccessor(Table table, List<VariableContext> order)	Initializes a new instance of the TableAccessor class, allowing random access to [table] with a specified [order] for the variables.
TableIterator(Table table, List<VariableContext> order)	Initializes a new instance of the TableIterator class, allowing sequential access to [table] with a specified [order] for the node variables.
VariableMap(VariableContextCollection sortedVariables, List<VariableContext> order)	Initializes a new instance of the VariableMap class.

Uses of VariableContext in com.bayesserver.analysis

Methods in com.bayesserver.analysis that return VariableContext

Modifier and Type	Method	Description
VariableContext	ValueOfInformationTestOutput.getVariable()	Gets the variable that was tested.

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

Modifier and Type	Method	Description
List<VariableContext>	AssociationPair.getX()	Gets the variable contexts in the first set.
List<VariableContext>	AssociationPair.getY()	Gets the varible contexts in the second set.

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

Modifier and Type	Method	Description
static ValueOfInformationOutput	ValueOfInformation.calculate(VariableContext hypothesis, List<VariableContext> testVariables, Evidence evidence, InferenceFactory factory, ValueOfInformationOptions options)	Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.

Method parameters in com.bayesserver.analysis with type arguments of type VariableContext

Modifier and Type	Method	Description
static ValueOfInformationOutput	ValueOfInformation.calculate(VariableContext hypothesis, List<VariableContext> testVariables, Evidence evidence, InferenceFactory factory, ValueOfInformationOptions options)	Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.

Constructor parameters in com.bayesserver.analysis with type arguments of type VariableContext

Constructor	Description
AssociationPair(List<VariableContext> x, List<VariableContext> y)	Initializes a new instance of the AssociationPair class with two sets of variable contexts.

Uses of VariableContext in com.bayesserver.inference

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

Modifier and Type	Method	Description
List<VariableContext>	CliqueDefinition.getVariableContexts()	The variables in the clique (optionally with times for DBNs).
List<VariableContext>	JunctionTreeNodeDefinition.getVariableContexts()	The variables in the clique or sepset (optionally with a time).
List<VariableContext>	SepsetDefinition.getVariableContexts()	The variables in the sepset (optionally with times for DBNs).

Uses of VariableContext in com.bayesserver.statistics

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

Modifier and Type	Method	Description
static double	MutualInformation.calculate(Distribution joint, VariableContext x, VariableContext y, LogarithmBase logarithmBase)	Measures the dependence between two variables.
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.

Method parameters in com.bayesserver.statistics with type arguments of type VariableContext

Modifier and Type	Method	Description
static double	Entropy.calculate(CLGaussian joint, List<VariableContext> conditionOn, LogarithmBase logarithmBase)	Measures the uncertainty of a distribution conditional on one or more 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(Table joint, List<VariableContext> conditionOn, LogarithmBase logarithmBase)	Measures the uncertainty of a distribution conditional on one or more variables.
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, 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.