java.lang.Object
- com.bayesserver.CLGaussian

All Implemented Interfaces:

Distribution
```
public final class CLGaussian
extends Object
implements Distribution
```
Represents a Conditional Linear Gaussian probability distribution. The distribution contains a Table distribution which represents any discrete combinations, and for each combination there exists a multivariate Gaussian distribution and weight/regression coefficients. Note that head variables are those that appear to the left of the bar in the expression P(A|B) and tail variables are those to the right.

Constructor Summary

Constructors
Constructor	Description
`CLGaussian(CLGaussian source)`	Initializes a new instance of the `CLGaussian` class, copying the source distribution.
`CLGaussian(CLGaussian source, Integer timeShift)`	Initializes a new instance of the `CLGaussian` class, copying the source distribution but shifting any times by the specified number of units.
`CLGaussian(Node node)`	Initializes a new instance of the `CLGaussian` class with the variables of a single node.
`CLGaussian(Node node, Integer time)`	Initializes a new instance of the `CLGaussian` class with the variables of a single node at the specified time.
`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(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].
`CLGaussian(Variable variable, Integer time)`	Initializes a new instance of the `CLGaussian` class with a single variable at the specified time.
`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.
`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].

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`Distribution`	`copy()`	Creates a copy of the distribution.
`Distribution`	`copy(Integer timeShift)`	Creates a copy of the distribution, and shifts any times associated with variables by the specified amount.
`void`	`copyFrom(CLGaussian source)`	Copies the values from the [source] distribution to this instance.
`CLGaussian`	`divide(CLGaussian subset)`	Creates a new distribution by dividing this instance by the [subset].
`Distribution`	`divide(Distribution subset)`	Creates a new distribution by dividing this instance by the [subset].
`double`	`getCovariance(int index, int sortedContinuousHeadA, int sortedContinuousHeadB)`	Gets the covariance of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`double`	`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`	`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`	`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`	`getCovariance(Variable continuousHeadA, Variable continuousHeadB)`	Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
`double`	`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`	`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`	`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`	`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`	`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`	`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`	`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).
`boolean`	`getLocked()`	Locks or unlocks a distribution.
`double`	`getMean(int index, int sortedContinuousHead)`	Gets the mean of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`double`	`getMean(Variable continuousHead)`	Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`double`	`getMean(VariableContext continuousHead, State... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	`getMean(VariableContext continuousHead, StateContext... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	`getMean(VariableContext continuousHead, TableIterator iterator)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`double`	`getMean(Variable continuousHead, State... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	`getMean(Variable continuousHead, StateContext... discrete)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
`double`	`getMean(Variable continuousHead, TableIterator iterator)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`double`	`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`	`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`	`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`	`getMean(Variable continuousHead, Integer time, TableIterator iterator)`	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
`Distribution`	`getOuter()`	Returns the parent distribution, if this instance is aggregated by another distribution.
`Node`	`getOwner()`	Gets the current owner, if assigned to a node.
`VariableContextCollection`	`getSortedContinuousHead()`	Gets the collection of continuous head variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
`VariableContextCollection`	`getSortedContinuousTail()`	Gets the collection of continuous tail variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
`VariableContextCollection`	`getSortedVariables()`	Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
`Table`	`getTable()`	Gets the `Table` which specifies the distribution over any discrete variables.
`double`	`getVariance(int index, int sortedContinuousHead)`	Gets the variance of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`double`	`getVariance(Variable continuousHead)`	Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`double`	`getVariance(VariableContext continuousHead, State... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	`getVariance(VariableContext continuousHead, StateContext... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	`getVariance(VariableContext continuousHead, TableIterator iterator)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	`getVariance(Variable continuousHead, State... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	`getVariance(Variable continuousHead, StateContext... discrete)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	`getVariance(Variable continuousHead, TableIterator iterator)`	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
`double`	`getVariance(Variable continuousHead, Integer time)`	Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`double`	`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`	`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`	`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`	`getWeight(int index, int sortedContinuousHead, int sortedContinuousTail)`	Gets the weight (regression coefficient) of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`double`	`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`	`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`	`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).
`double`	`getWeight(Variable continuousHead, Variable continuousTail)`	Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
`double`	`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`	`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`	`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`	`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`	`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`	`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`	`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).
`CLGaussian`	`instantiate(Variable variable, double value)`	Calculates the distribution which results from instantiating a particular variable.
`CLGaussian`	`instantiate(Variable variable, double value, Integer time)`	Calculates the distribution which results from instantiating a particular variable at a specified time.
`Distribution`	`instantiate(Double[] values)`	Calculates the distribution which results from instantiating a number of variables.
`CLGaussian`	`instantiateDiscrete(Integer[] discreteValues)`	Instantiates discrete variables.
`Table`	`instantiateHead(double[] headValues, double[] logPdf)`	Instantiates all continuous head variable contexts.
`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`	`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.
`Distribution`	`instantiateHeads(Double[] headValues, double[] logPdf)`	Instantiates continuous head variable contexts.
`CLGaussian`	`instantiateTails(Double[] tailValues)`	Calculates the distribution which results from instantiating continuous tail variables.
`boolean`	`isReadOnly()`	Indicates whether the distribution is read only.
`void`	`marginalize(CLGaussian superset)`	Marginalizes (sums/integrates) the [superset] into this instance.
`void`	`marginalize(Distribution superset)`	Marginalizes (integrates) the [superset] into this instance.
`void`	`marginalize(Distribution superset, PropagationMethod propagation)`	Marginalizes (integrates) the [superset] into this instance.
`void`	`marginalizeTo(Table table)`	Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
`void`	`marginalizeTo(Table table, PropagationMethod propagation)`	Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
`CLGaussian`	`multiply(CLGaussian gaussian)`	Multiplies this instance by another `CLGaussian` distribution.
`Distribution`	`multiply(Distribution distribution)`	Multiplies this instance by another distribution.
`void`	`reset()`	Resets all mean, covariance and weight entries to zero.
`void`	`setCovariance(int index, int sortedContinuousHeadA, int sortedContinuousHeadB, double value)`	Sets the covariance value of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`void`	`setCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, double value)`	Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
`void`	`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`	`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`	`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`	`setCovariance(Variable continuousHeadA, Variable continuousHeadB, double value)`	Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
`void`	`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`	`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`	`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`	`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`	`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`	`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`	`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`	`setLocked(boolean value)`	Locks or unlocks a distribution.
`void`	`setMean(int index, int sortedContinuousHead, double value)`	Sets the mean value of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`void`	`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`	`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`	`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`	`setMean(Variable continuousHead, double value)`	Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	`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`	`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`	`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`	`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`	`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`	`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`	`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`	`setVariance(int index, int sortedContinuousHead, double value)`	Sets the variance value of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`void`	`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`	`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`	`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`	`setVariance(Variable continuousHead, double value)`	Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	`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`	`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`	`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`	`setVariance(Variable continuousHead, Integer time, double value)`	Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
`void`	`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`	`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`	`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`	`setWeight(int index, int sortedContinuousHead, int sortedContinuousTail, double value)`	Sets the weight/regression coefficient of the Gaussian distribution at the specified [index] in the `Table` of discrete combinations.
`void`	`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`	`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`	`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).
`void`	`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`	`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`	`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`	`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`	`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`	`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`	`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`	`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`	`timeShift(int units)`	Shifts any times associated with the distribution variables by the specified number of time units.
`String`	`toString()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - CLGaussian
```
public CLGaussian(List<VariableContext> variableContexts)
```
    Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts]. Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
    
    Parameters:
    
    variableContexts - The variable contexts containing the distribution variables.
    
    Throws:
    
    NullPointerException - Raised if [variableContexts] is null.
  - CLGaussian
```
public CLGaussian(List<VariableContext> variableContexts,
                  HeadTail headTail)
```
    Initializes a new instance of the CLGaussian class with the variables specified in [variableContexts]. Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
    
    Parameters:
    
    variableContexts - The variable contexts containing the distribution variables.
    
    headTail - Overrides the Head or Tail value found in each VariableContext.
    
    Throws:
    
    NullPointerException - Raised if [variableContexts] is null.
  - CLGaussian
```
public CLGaussian(VariableContext[] variableContexts)
```
    Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts]. Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
    
    Parameters:
    
    variableContexts - The variable contexts containing the distribution variables.
    
    Throws:
    
    NullPointerException - Raised if [variableContexts] is null.
  - CLGaussian
```
public CLGaussian(VariableContext[] variableContexts,
                  int count)
```
    Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts]. Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
    
    Parameters:
    
    variableContexts - The variable contexts containing the distribution variables.
    
    count - The number of items to include from [variableContexts].
    
    Throws:
    
    NullPointerException - Raised if [variableContexts] is null.
  - CLGaussian
```
public CLGaussian(VariableContext[] variableContexts,
                  int count,
                  HeadTail headTail)
```
    Initializes a new instance of the CLGaussian class with [count] variables specified in [variableContexts]. Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
    
    Parameters:
    
    variableContexts - The variable contexts containing the distribution variables.
    
    count - The number of items to include from [variableContexts].
    
    headTail - Overrides the Head or Tail value found in each VariableContext.
    
    Throws:
    
    NullPointerException - Raised if [variableContexts] is null.
  - CLGaussian
```
public CLGaussian(Node node,
                  Integer time)
```
    Initializes a new instance of the CLGaussian class with the variables of a single node at the specified time. Variables are assumed to be head variables.
    
    Parameters:
    
    node - The node whose variables will belong to the new distribution.
    
    time - The time for any temporal nodes/variables.
    
    Throws:
    
    NullPointerException - Raised if [node] is null.
  - CLGaussian
```
public CLGaussian(List<Variable> variables,
                  Integer time)
```
    Initializes a new instance of the CLGaussian class with the specified variables at a particular time. Variables are assumed to be head variables.
    
    Parameters:
    
    variables - The variables for the new distribution.
    
    time - The time for any temporal nodes/variables.
    
    Throws:
    
    NullPointerException - Raised if [variables] is null.
    
    IllegalArgumentException - Raised if an element of [variables] is null or a variable does not belong to a network.
  - CLGaussian
```
public CLGaussian(List<Variable> variables,
                  Integer time,
                  HeadTail headTail)
```
    Initializes a new instance of the CLGaussian class with the specified variables.
    
    Parameters:
    
    variables - The variables for the new distribution.
    
    time - The time for any temporal nodes/variables.
    
    headTail - Specifies whether the variables should be marked as Head or Tail.
    
    Throws:
    
    NullPointerException - Raised if [variables] is null.
    
    IllegalArgumentException - Raised if an element of [variables] is null or a variable does not belong to a network.
  - CLGaussian
```
public CLGaussian(Variable[] variables)
```
    Initializes a new instance of the CLGaussian class with the specified variables. Variables are assumed to be head variables.
    
    Parameters:
    
    variables - The variables for the new distribution.
  - CLGaussian
```
public CLGaussian(Node node)
```
    Initializes a new instance of the CLGaussian class with the variables of a single node. Variables are assumed to be head variables.
    
    Parameters:
    
    node - The node whose variables will belong to the new distribution.
  - CLGaussian
```
public CLGaussian(Variable variable)
```
    Initializes a new instance of the CLGaussian class with a single variable. The variable is assumed to be a head variable.
    
    Parameters:
    
    variable - The variable that will belong to the new distribution.
  - CLGaussian
```
public CLGaussian(VariableContext variableContext)
```
    Initializes a new instance of the CLGaussian class from a single VariableContext.
    
    Parameters:
    
    variableContext - The variable context.
    
    Throws:
    
    NullPointerException - Raised when [variableContext] is null.
  - CLGaussian
```
public CLGaussian(Variable variable,
                  Integer time)
```
    Initializes a new instance of the CLGaussian class with a single variable at the specified time. The variable is assumed to be a head variable.
    
    Parameters:
    
    variable - The variable that will belong to the new distribution.
    
    time - The time associated with the variable.
    
    Throws:
    
    NullPointerException - Raised if [variable] is null.
  - CLGaussian
```
public CLGaussian(CLGaussian source)
```
    Initializes a new instance of the CLGaussian class, copying the source distribution.
    
    Parameters:
    
    source - The distribution to copy.
  - CLGaussian
```
public CLGaussian(CLGaussian source,
                  Integer timeShift)
```
    Initializes a new instance of the CLGaussian class, copying the source distribution but shifting any times by the specified number of units.
    
    Parameters:
    
    source - The distribution to copy.
    
    timeShift - The number of units to adjust any times associated with variables.
- Method Detail
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - reset
```
public void reset()
```
    Resets all mean, covariance and weight entries to zero.
  - timeShift
```
public void timeShift(int units)
```
    Shifts any times associated with the distribution variables by the specified number of time units.
    
    Specified by:
    
    timeShift in interface Distribution
    
    Parameters:
    
    units - The number of time units to shift. Can be negative if required.
  - getLocked
```
public boolean getLocked()
```
    Locks or unlocks a distribution. The distribution cannot be modified if either Distribution.getLocked() is true or Distribution.getOwner() is not null.
    
    Specified by:
    
    getLocked in interface Distribution
    
    Returns:
    
    true if locked; otherwise, false.
  - setLocked
```
public void setLocked(boolean value)
```
    Locks or unlocks a distribution. The distribution cannot be modified if either Distribution.getLocked() is true or Distribution.getOwner() is not null.
    
    Specified by:
    
    setLocked in interface Distribution
    
    Parameters:
    
    value - true if locked; otherwise, false.
  - isReadOnly
```
public boolean isReadOnly()
```
    Indicates whether the distribution is read only. A distribution is read only if it is locked or is assigned to a Node.
    
    Specified by:
    
    isReadOnly in interface Distribution
    
    Returns:
    
    true if read only; otherwise, false.
  - copy
```
public Distribution copy()
```
    Creates a copy of the distribution. The new distribution will not have an owner.
    
    Specified by:
    
    copy in interface Distribution
    
    Returns:
    
    A copy of this instance.
  - copy
```
public Distribution copy(Integer timeShift)
```
    Creates a copy of the distribution, and shifts any times associated with variables by the specified amount. The new distribution will not have an owner.
    
    Specified by:
    
    copy in interface Distribution
    
    Parameters:
    
    timeShift - The amount to shift any times present in the distribution. Can be negative.
    
    Returns:
    
    A copy of this instance, with shifted times.
  - getMean
```
public double getMean(int index,
                      int sortedContinuousHead)
```
    Gets the mean of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHead - The position of the required continuous head variable.
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      State... discrete)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead)
```
    Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      Integer time)
```
    Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      Integer time,
                      State... discrete)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(VariableContext continuousHead,
                      State... discrete)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      StateContext... discrete)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      Integer time,
                      StateContext... discrete)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(VariableContext continuousHead,
                      StateContext... discrete)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      TableIterator iterator)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(Variable continuousHead,
                      Integer time,
                      TableIterator iterator)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The mean value.
  - getMean
```
public double getMean(VariableContext continuousHead,
                      TableIterator iterator)
```
    Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The mean value.
  - getVariance
```
public double getVariance(int index,
                          int sortedContinuousHead)
```
    Gets the variance of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHead - The position of the required continuous head variable.
    
    Returns:
    
    The variance.
  - getVariance
```
public double getVariance(Variable continuousHead,
                          State... discrete)
```
    Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(Variable continuousHead)
```
    Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(Variable continuousHead,
                          Integer time)
```
    Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    Returns:
    
    The variance value.
  - getVariance
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(VariableContext continuousHead,
                          State... discrete)
```
    Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(Variable continuousHead,
                          StateContext... discrete)
```
    Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(VariableContext continuousHead,
                          StateContext... discrete)
```
    Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(Variable continuousHead,
                          TableIterator iterator)
```
    Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The variance value.
  - getVariance
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The variance value.
  - getVariance
```
public double getVariance(VariableContext continuousHead,
                          TableIterator iterator)
```
    Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The variance value.
  - getCovariance
```
public double getCovariance(int index,
                            int sortedContinuousHeadA,
                            int sortedContinuousHeadB)
```
    Gets the covariance of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHeadA - The position of the first continuous head variable.
    
    sortedContinuousHeadB - The position of the second continuous head variable.
    
    Returns:
    
    The covariance entry.
  - getCovariance
```
public double getCovariance(Variable continuousHeadA,
                            Variable continuousHeadB,
                            State... discrete)
```
    Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(Variable continuousHeadA,
                            Variable continuousHeadB)
```
    Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(Variable continuousHeadA,
                            Integer timeA,
                            Variable continuousHeadB,
                            Integer timeB)
```
    Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(Variable continuousHeadA,
                            Variable continuousHeadB,
                            StateContext... discrete)
```
    Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(VariableContext continuousHeadA,
                            VariableContext continuousHeadB,
                            State... discrete)
```
    Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(VariableContext continuousHeadA,
                            VariableContext continuousHeadB,
                            StateContext... discrete)
```
    Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(Variable continuousHeadA,
                            Variable continuousHeadB,
                            TableIterator iterator)
```
    Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The covariance value.
  - getCovariance
```
public double getCovariance(VariableContext continuousHeadA,
                            VariableContext continuousHeadB,
                            TableIterator iterator)
```
    Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The covariance value.
  - getWeight
```
public double getWeight(int index,
                        int sortedContinuousHead,
                        int sortedContinuousTail)
```
    Gets the weight (regression coefficient) of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHead - The position of the required continuous head variable.
    
    sortedContinuousTail - The position of the required continuous tail variable.
    
    Returns:
    
    The weight / regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable and time (if any) from T in the expression P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double getWeight(Variable continuousHead,
                        Variable continuousTail)
```
    Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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].
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable and time (if any) from T in the expression P(H|T).
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The weight/regression coefficient.
  - getWeight
```
public double 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
    
    Returns:
    
    The weight/regression coefficient.
  - setMean
```
public void setMean(int index,
                    int sortedContinuousHead,
                    double value)
```
    Sets the mean value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHead - The position of the required continuous head variable.
    
    value - The mean value.
  - setMean
```
public void setMean(Variable continuousHead,
                    double value,
                    State... discrete)
```
    Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The mean value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setMean
```
public void 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.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The mean value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setMean
```
public void setMean(VariableContext continuousHead,
                    double value,
                    State... discrete)
```
    Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The mean value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setMean
```
public void setMean(Variable continuousHead,
                    double value,
                    StateContext... discrete)
```
    Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The mean value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setMean
```
public void setMean(Variable continuousHead,
                    double value)
```
    Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The mean value.
  - setMean
```
public void setMean(Variable continuousHead,
                    Integer time,
                    double value)
```
    Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The mean value.
  - setMean
```
public void 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.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The mean value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setMean
```
public void setMean(VariableContext continuousHead,
                    double value,
                    StateContext... discrete)
```
    Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The mean value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setMean
```
public void setMean(Variable continuousHead,
                    double value,
                    TableIterator iterator)
```
    Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The mean value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setMean
```
public void 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.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The mean value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setMean
```
public void setMean(VariableContext continuousHead,
                    double value,
                    TableIterator iterator)
```
    Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The mean value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setCovariance
```
public void setCovariance(int index,
                          int sortedContinuousHeadA,
                          int sortedContinuousHeadB,
                          double value)
```
    Sets the covariance value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHeadA - The position of the first continuous head variable.
    
    sortedContinuousHeadB - The position of the second continuous head variable.
    
    value - The covariance value to copy.
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    value - The covariance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    value - The covariance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setCovariance
```
public void 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]
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    value - The covariance value.
  - setCovariance
```
public void setCovariance(Variable continuousHeadA,
                          Variable continuousHeadB,
                          double value)
```
    Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setCovariance
```
public void setCovariance(VariableContext continuousHeadA,
                          VariableContext continuousHeadB,
                          double value)
```
    Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable from H in the expression P(H) or P(H|T).
    
    timeA - The time of the first continuous head variable, or null if not a temporal variable.
    
    continuousHeadB - A second continuous head variable from H in the expression P(H) or P(H|T).
    
    timeB - The time of the second continuous head variable, or null if not a temporal variable.
    
    value - The covariance value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setCovariance
```
public void 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).
    
    Parameters:
    
    continuousHeadA - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    continuousHeadB - A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The covariance value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setVariance
```
public void setVariance(int index,
                        int sortedContinuousHead,
                        double value)
```
    Sets the variance value of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHead - The position of the required continuous head variable.
    
    value - The variance value to set.
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The variance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The variance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The variance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The variance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setVariance
```
public void setVariance(Variable continuousHead,
                        double value)
```
    Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The variance value.
  - setVariance
```
public void setVariance(Variable continuousHead,
                        Integer time,
                        double value)
```
    Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The variance value.
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The variance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The variance value.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    value - The variance value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H) or P(H|T).
    
    time - The time of the continuous head variable, or null if not a temporal variable.
    
    value - The variance value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setVariance
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
    
    value - The variance value.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setWeight
```
public void setWeight(int index,
                      int sortedContinuousHead,
                      int sortedContinuousTail,
                      double value)
```
    Sets the weight/regression coefficient of the Gaussian distribution at the specified [index] in the Table of discrete combinations.
    
    Parameters:
    
    index - The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
    
    sortedContinuousHead - The position of the required continuous head variable.
    
    sortedContinuousTail - The position of the required continuous tail variable.
    
    value - The weight to copy.
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    value - The weight/regression coefficient.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and associated time (if any) from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable and associated time (if any) from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setWeight
```
public void 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].
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    value - The weight/regression coefficient.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setWeight
```
public void 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].
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    value - The weight/regression coefficient.
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and associated time (if any) from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable and associated time (if any) from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
    
    discrete - The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable from H in the expression P(H|T).
    
    timeHead - The time of the continuous head variable, or null if not a temporal variable.
    
    continuousTail - A continuous tail variable from T in the expression P(H|T).
    
    timeTail - The time of the continuous tail variable, or null if not a temporal variable.
    
    value - The weight/regression coefficient.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - setWeight
```
public void 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).
    
    Parameters:
    
    continuousHead - A continuous head variable and associated time (if any) from H in the expression P(H|T).
    
    continuousTail - A continuous tail variable and associated time (if any) from T in the expression P(H|T).
    
    value - The weight/regression coefficient.
    
    iterator - The discrete combination (mixture) identified by the position of the iterator.
  - getSortedVariables
```
public VariableContextCollection getSortedVariables()
```
    Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
    
    Specified by:
    
    getSortedVariables in interface Distribution
    
    Returns:
    
    Variables sorted by time and the order in which variables were created.
  - getSortedContinuousHead
```
public VariableContextCollection getSortedContinuousHead()
```
    Gets the collection of continuous head variables in the distribution, sorted by time (which may be null) and the order in which variables were created. Note that head variables are those that appear to the left of the bar in the expression P(A|B) and tail variables are those to the right.
    
    Returns:
    
    Continuous head variables sorted by time, and the order in which variables were created.
    
    See Also:
    
    VariableMap
  - getSortedContinuousTail
```
public VariableContextCollection getSortedContinuousTail()
```
    Gets the collection of continuous tail variables in the distribution, sorted by time (which may be null) and the order in which variables were created. Note that head variables are those that appear to the left of the bar in the expression P(A|B) and tail variables are those to the right.
    
    Returns:
    
    Continuous tail variables sorted by time and the order in which variables were created.
    
    See Also:
    
    VariableMap
  - getOwner
```
public Node getOwner()
```
    Gets the current owner, if assigned to a node. A distribution cannot be modified when it is assigned to a node.
    
    Specified by:
    
    getOwner in interface Distribution
    
    Returns:
    
    The owner, or null if not assigned to a node.
  - getOuter
```
public Distribution getOuter()
```
    Description copied from interface: Distribution
    
    Returns the parent distribution, if this instance is aggregated by another distribution.
    
    Specified by:
    
    getOuter in interface Distribution
  - getTable
```
public Table getTable()
```
    Gets the Table which specifies the distribution over any discrete variables.
    
    Specified by:
    
    getTable in interface Distribution
    
    Returns:
    
    The table.
  - copyFrom
```
public void copyFrom(CLGaussian source)
```
    Copies the values from the [source] distribution to this instance. The variable counts between distributions must match but the variable contexts need not be equal.
    
    Parameters:
    
    source - The source distribution from which values are copied.
  - divide
```
public Distribution divide(Distribution subset)
```
    Creates a new distribution by dividing this instance by the [subset]. Also known as the complement. If the resulting distribution were subsequently multiplied by [subset], the result would be equivalent to this instance.
    
    Specified by:
    
    divide in interface Distribution
    
    Parameters:
    
    subset - The subset to divide by.
    
    Returns:
    
    The new distribution (complement).
    
    Throws:
    
    NullPointerException - Raised if [subset] is null.
    
    UnsupportedOperationException - If subset is not a subset of variables in this instance.
  - divide
```
public CLGaussian divide(CLGaussian subset)
```
    Creates a new distribution by dividing this instance by the [subset]. Also known as the complement. If the resulting distribution were subsequently multiplied by [subset], the result would be equivalent to this instance.
    
    Parameters:
    
    subset - The subset to divide by.
    
    Returns:
    
    The new distribution (complement).
    
    Throws:
    
    NullPointerException - Raised if [subset] is null.
    
    UnsupportedOperationException - If subset is not a subset of variables in this instance.
  - instantiateDiscrete
```
public CLGaussian instantiateDiscrete(Integer[] discreteValues)
```
    Instantiates discrete variables.
    
    Parameters:
    
    discreteValues - A discrete value (or null) for each discrete variable in the Gaussian table.
    
    Returns:
    
    The instantiated distribution.
  - instantiate
```
public Distribution instantiate(Double[] values)
```
    Calculates the distribution which results from instantiating a number of variables. [values] should contain one entry for each
    
    Specified by:
    
    instantiate in interface Distribution
    
    Parameters:
    
    values - The instantiated values. Entries can be null.
    
    Returns:
    
    The instantiated distribution.
    
    See Also:
    
    in the distribution, however entries can be null.
  - instantiateHeads
```
public Distribution instantiateHeads(Double[] headValues,
                                     double[] logPdf)
```
    Instantiates continuous head variable contexts.
    
    Parameters:
    
    headValues - The value (or null) for each continuous head variable context.
    
    logPdf - Optional array of length Table.size() that is filled with the logged pdf values, useful when pdf values are zero.
    
    Returns:
    
    The instantiated distribution.
  - instantiateTails
```
public CLGaussian instantiateTails(Double[] tailValues)
```
    Calculates the distribution which results from instantiating continuous tail variables.
    
    Parameters:
    
    tailValues - The value (or null) for each continuous tail.
    
    Returns:
    
    The instantiated distribution.
  - instantiate
```
public CLGaussian instantiate(Variable variable,
                              double value)
```
    Calculates the distribution which results from instantiating a particular variable.
    
    Parameters:
    
    variable - The variable to instantiate.
    
    value - The instantiated value.
    
    Returns:
    
    The instantiated distribution.
    
    Throws:
    
    NullPointerException - Raised if [variable] is null.
  - instantiate
```
public CLGaussian instantiate(Variable variable,
                              double value,
                              Integer time)
```
    Calculates the distribution which results from instantiating a particular variable at a specified time.
    
    Parameters:
    
    variable - The variable to instantiate.
    
    value - The instantiated value.
    
    time - The time associated with the variable. Can be null.
    
    Returns:
    
    The instantiated distribution.
    
    Throws:
    
    NullPointerException - Raised if [variable] is null.
  - instantiateHead
```
public CLGaussian instantiateHead(Variable variable,
                                  double value,
                                  Integer time)
```
    Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
    
    Parameters:
    
    variable - The variable to instantiate.
    
    value - The instantiated value.
    
    time - The time associated with the variable. Can be null.
    
    Returns:
    
    The instantiated distribution.
    
    Throws:
    
    NullPointerException - Raised if [variable] is null.
  - instantiateHead
```
public 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.
    
    Parameters:
    
    variable - The variable to instantiate.
    
    value - The instantiated value.
    
    time - The time associated with the variable. Can be null.
    
    logPdf - A buffer of length Table.size()that is filled with the log of the pdf values calculated during instantiation. Can be null.
    
    Returns:
    
    The instantiated distribution.
    
    Throws:
    
    NullPointerException - Raised if [variable] is null.
  - instantiateHead
```
public Table instantiateHead(double[] headValues,
                             double[] logPdf)
```
    Instantiates all continuous head variable contexts.
    
    Parameters:
    
    headValues - The values for the continuous head variable contexts.
    
    logPdf - Optional array of length Table.size() that is filled with the logged pdf values, useful when pdf values are zero.
    
    Returns:
    
    The instantiated distribution.
  - marginalize
```
public void marginalize(Distribution superset)
```
    Marginalizes (integrates) the [superset] into this instance.
    
    Specified by:
    
    marginalize in interface Distribution
    
    Parameters:
    
    superset - A distribution whose variables form a superset of the variables in this instance.
    
    Throws:
    
    NullPointerException - Raised if [superset] is null.
    
    IllegalStateException - Raised if this instance is read only, or if any variables are no longer correctly due to modifications of the network.
    
    IllegalArgumentException - Raised if [superset] does not contain all the variables in this instance.
  - marginalize
```
public void marginalize(Distribution superset,
                        PropagationMethod propagation)
```
    Marginalizes (integrates) the [superset] into this instance.
    
    Specified by:
    
    marginalize in interface Distribution
    
    Parameters:
    
    superset - A distribution whose variables form a superset of the variables in this instance.
    
    propagation - The propagation method to use during marginalization.
    
    Throws:
    
    NullPointerException - Raised if [superset] is null.
    
    IllegalStateException - Raised if this instance is read only, or if any variables are no longer correctly due to modifications of the network.
    
    IllegalArgumentException - Raised if [superset] does not contain all the variables in this instance.
  - marginalize
```
public void marginalize(CLGaussian superset)
```
    Marginalizes (sums/integrates) the [superset] into this instance.
    
    Parameters:
    
    superset - A CLGaussian whose variables form a superset of the variables in this instance.
    
    Throws:
    
    NullPointerException - Raised if [superset] is null.
    
    IllegalStateException - Raised if this instance is read only, or if any variables are no longer sorted correctly due to modifications of the network.
    
    IllegalArgumentException - Raised if [superset] does not contain all the variables in this instance.
  - marginalizeTo
```
public void marginalizeTo(Table table)
```
    Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
    
    Parameters:
    
    table - A Table whose variables form a subset of discrete variables in this instance.
    
    Throws:
    
    NullPointerException - Raised if [table] is null.
  - marginalizeTo
```
public void marginalizeTo(Table table,
                          PropagationMethod propagation)
```
    Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
    
    Parameters:
    
    table - A Table whose variables form a subset of discrete variables in this instance.
    
    propagation - The propagation method to use during marginalization.
    
    Throws:
    
    NullPointerException - Raised if [table] is null.
  - multiply
```
public CLGaussian multiply(CLGaussian gaussian)
```
    Multiplies this instance by another CLGaussian distribution.
    
    Parameters:
    
    gaussian - The distribution to combine.
    
    Returns:
    
    The combined distribution.
    
    Throws:
    
    NullPointerException - Raised if [gaussian] is null.
  - multiply
```
public Distribution multiply(Distribution distribution)
```
    Multiplies this instance by another distribution.
    
    Specified by:
    
    multiply in interface Distribution
    
    Parameters:
    
    distribution - The distribution to combine.
    
    Returns:
    
    The combined distribution.
    
    Throws:
    
    NullPointerException - Raised if [distribution] is null.

Class CLGaussian

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

CLGaussian

Method Detail

toString

reset

timeShift

getLocked

setLocked

isReadOnly

copy

copy

getMean

getMean

getMean

getMean

getMean

getMean

getMean

getMean

getMean

getMean

getMean

getMean

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getVariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getCovariance

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

getWeight

setMean

setMean

setMean