Uses of Class
com.bayesserver.TableIterator

Packages that use TableIterator

Package	Description
com.bayesserver

Uses of TableIterator in com.bayesserver

Methods in com.bayesserver with parameters of type TableIterator

Modifier and Type	Method	Description
double	CLGaussian.getCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, TableIterator iterator)	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
double	CLGaussian.getCovariance(Variable continuousHeadA, Variable continuousHeadB, TableIterator iterator)	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
double	CLGaussian.getCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, TableIterator iterator)	Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
double	CLGaussian.getMean(VariableContext continuousHead, TableIterator iterator)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
double	CLGaussian.getMean(Variable continuousHead, TableIterator iterator)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
double	CLGaussian.getMean(Variable continuousHead, Integer time, TableIterator iterator)	Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
double	CLGaussian.getVariance(VariableContext continuousHead, TableIterator iterator)	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
double	CLGaussian.getVariance(Variable continuousHead, TableIterator iterator)	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
double	CLGaussian.getVariance(Variable continuousHead, Integer time, TableIterator iterator)	Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
double	CLGaussian.getWeight(VariableContext continuousHead, VariableContext continuousTail, TableIterator iterator)	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
double	CLGaussian.getWeight(Variable continuousHead, Variable continuousTail, TableIterator iterator)	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
double	CLGaussian.getWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, TableIterator iterator)	Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
void	CLGaussian.setCovariance(VariableContext continuousHeadA, VariableContext continuousHeadB, double value, TableIterator iterator)	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
void	CLGaussian.setCovariance(Variable continuousHeadA, Variable continuousHeadB, double value, TableIterator iterator)	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
void	CLGaussian.setCovariance(Variable continuousHeadA, Integer timeA, Variable continuousHeadB, Integer timeB, double value, TableIterator iterator)	Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
void	CLGaussian.setMean(VariableContext continuousHead, double value, TableIterator iterator)	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
void	CLGaussian.setMean(Variable continuousHead, double value, TableIterator iterator)	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
void	CLGaussian.setMean(Variable continuousHead, Integer time, double value, TableIterator iterator)	Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
void	CLGaussian.setVariance(VariableContext continuousHead, double value, TableIterator iterator)	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
void	CLGaussian.setVariance(Variable continuousHead, double value, TableIterator iterator)	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
void	CLGaussian.setVariance(Variable continuousHead, Integer time, double value, TableIterator iterator)	Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
void	CLGaussian.setWeight(VariableContext continuousHead, VariableContext continuousTail, double value, TableIterator iterator)	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
void	CLGaussian.setWeight(Variable continuousHead, Variable continuousTail, double value, TableIterator iterator)	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
void	CLGaussian.setWeight(Variable continuousHead, Integer timeHead, Variable continuousTail, Integer timeTail, double value, TableIterator iterator)	Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).