Package | Description |
---|---|
com.bayesserver | |
com.bayesserver.analysis | |
com.bayesserver.statistics |
Modifier and Type | Method and 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. |
Modifier and Type | Method and 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).
|
Modifier and Type | Method and Description |
---|---|
boolean |
VariableContextCollection.containsAll(List<VariableContext> items,
boolean ignoreHeadTail)
Determines whether all [items] are matched in the collection at the specified times.
|
Constructor and 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 and 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. |
Modifier and Type | Method and Description |
---|---|
VariableContext |
ValueOfInformationTestOutput.getVariable()
Gets the variable that was tested.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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 and Description |
---|
AssociationPair(List<VariableContext> x,
List<VariableContext> y)
Initializes a new instance of the
AssociationPair class with two sets of variable contexts. |
AssociationPair(List<VariableContext> x,
List<VariableContext> y)
Initializes a new instance of the
AssociationPair class with two sets of variable contexts. |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and 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 |
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 |
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 |
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 |
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 |
Entropy.calculate(Table joint,
List<VariableContext> conditionOn,
LogarithmBase logarithmBase)
Measures the uncertainty of a distribution conditional on one or more variables.
|
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