Uses of Class
com.bayesserver.State

Packages that use State

Package	Description
com.bayesserver
com.bayesserver.analysis
com.bayesserver.causal
com.bayesserver.inference
com.bayesserver.optimization

Uses of State in com.bayesserver

Methods in com.bayesserver that return State

Modifier and Type	Method	Description
State	State.copy()	Copies this instance.
State	StateCollection.findByValue(Object value)	Finds the state whose value/> matches the given [value], or null if a match is not found.
State	Variable.findStateByValue(Object value)	Finds a state based on a state value.
State	StateCollection.get(int index)	Gets the State at the specified index.
State	StateCollection.get(String name)	Performs a case sensitive lookup.
State	StateCollection.get(String name, boolean throwIfNotFound)	Performs a case sensitive lookup.
State	StateContext.getState()	Gets the State.
State	StateCollection.remove(int index)	Removes an element from the collection at the specified index.
State	StateCollection.set(int index, State value)	Sets the State at the specified index.

Methods in com.bayesserver with parameters of type State

Modifier and Type	Method	Description
void	StateCollection.add(int index, State item)	Inserts an element into the collection at the specified index.
double	Table.get(State... states)	Gets the table value corresponding to the given states.
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(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, 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.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(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, Integer time, State... discrete)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
int	Table.getSortedIndex(State... states)	Gets the index of the table element that corresponds to a particular combination of states.
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(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, Integer time, State... discrete)	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(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, 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).
State	StateCollection.set(int index, State value)	Sets the State at the specified index.
void	Table.set(double value, State... states)	Sets the table value corresponding to the given states.
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(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, 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.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(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, 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.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(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, 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.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(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, 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	NetworkMonitor.statesCollectionChange(Variable variable, int index, State add, State remove, CollectionAction action, boolean complete)	For internal use.

Constructors in com.bayesserver with parameters of type State

Constructor	Description
Node(String name, State... states)	Initializes a new instance of the Node class, with the name of the node, automatically creating an associated discrete Variable and adds the states specified in [states] to that variable.
StateContext(State state, Integer time)	Initializes a new instance of StateContext.
Variable(String name, State... states)	Initializes a new instance of the Variable class, with VariableValueType discrete and the specified name and adds the states specified in [states].

Uses of State in com.bayesserver.analysis

Methods in com.bayesserver.analysis that return State

Modifier and Type	Method	Description
State	AutoInsightStateOutput.getState()	Gets the state this insight refers to.
State[]	ParameterReference.getStates()	Gets the states which together locate a specific parameter in the node's distribution.
State	AutoInsightOutput.getTarget()	Gets the target state used to calculate the insight.

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

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, 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 double[][]	Correlation.fromCovariance(CLGaussian gaussian, State... states)
SensitivityFunctionOneWay	SensitivityToParameters.oneWay(Evidence evidence, State hypothesis, ParameterReference parameter)	Calculates how a hypothesis varies based on changes to a single parameter.
SensitivityFunctionTwoWay	SensitivityToParameters.twoWay(Evidence evidence, State hypothesis, ParameterReference parameter1, ParameterReference parameter2)	Calculates how a hypothesis varies based on changes to two parameters.

Constructors in com.bayesserver.analysis with parameters of type State

Constructor	Description
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 State in com.bayesserver.causal

Methods in com.bayesserver.causal that return State

Modifier and Type	Method	Description
State	EffectsAnalysisOutputItem.getTreatmentState()	Gets the treatment state used to measure the causal effect on the treatment.

Uses of State in com.bayesserver.inference

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

Modifier and Type	Method	Description
void	DefaultEvidence.setState(State state)	Sets evidence on a discrete state (hard evidence).
void	DefaultEvidence.setState(State state, Integer time)	Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
void	DefaultEvidence.setState(State state, Integer time, InterventionType interventionType)	Sets evidence on a discrete state (hard evidence), in the form of an intervention (do-operator).
void	Evidence.setState(State state)	Sets evidence on a discrete state (hard evidence).
void	Evidence.setState(State state, Integer time)	Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
void	Evidence.setState(State state, Integer time, InterventionType interventionType)	Sets evidence on a discrete state (hard evidence), in the form of an intervention (do-operator).

Uses of State in com.bayesserver.optimization

Methods in com.bayesserver.optimization that return State

Modifier and Type	Method	Description
State	DesignState.getState()	Gets the state these options refer to.
State	Objective.getState()	Gets the state being optimized.

Constructors in com.bayesserver.optimization with parameters of type State

Constructor	Description
DesignState(State state, Double lowerBound, Double upperBound)	Initializes a new instance of the com.bayesserver.optization.DesignState class.
Objective(State state, ObjectiveKind kind)	Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.
Objective(State state, ObjectiveKind kind, Double value)	Initializes a new instance of the {@link com.bayesserver.optimization.objective.} class.