Package edu.cmu.tetrad.search

Class ConditionalGaussianLikelihood

java.lang.Object

edu.cmu.tetrad.search.ConditionalGaussianLikelihood

public class ConditionalGaussianLikelihood
extends Object

Implements a conditional Gaussian likelihood. Please note that this this likelihood will be maximal only if the the continuous mixedVariables are jointly Gaussian conditional on the discrete mixedVariables; in all other cases, it will be less than maximal. For an algorithm like FGS this is fine.

Author:

Joseph Ramsey

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	ConditionalGaussianLikelihood.Ret	A return value for a likelihood--returns a likelihood value and the degrees of freedom for it.

Constructor Summary

Constructors

Constructor	Description
ConditionalGaussianLikelihood(DataSet dataSet)	Constructs the score using a covariance matrix.

Method Summary

Modifier and Type	Method	Description
ConditionalGaussianLikelihood.Ret	getLikelihood(int i, int[] parents)	Returns the likelihood of variable i conditional on the given parents, assuming the continuous mixedVariables index by i or by the parents are jointly Gaussian conditional on the discrete comparison.
double	getPenaltyDiscount()
void	setDiscretize(boolean discretize)
void	setNumCategoriesToDiscretize(int numCategoriesToDiscretize)
void	setPenaltyDiscount(double penaltyDiscount)
void	setRows(List<Integer> rows)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

ConditionalGaussianLikelihood

public ConditionalGaussianLikelihood(DataSet dataSet)

Constructs the score using a covariance matrix.

Method Details

setRows

public void setRows(List<Integer> rows)

getLikelihood

public ConditionalGaussianLikelihood.Ret getLikelihood(int i,
 int[] parents)

Returns the likelihood of variable i conditional on the given parents, assuming the continuous mixedVariables index by i or by the parents are jointly Gaussian conditional on the discrete comparison.

Parameters:

i - The index of the conditioned variable.

parents - The indices of the conditioning mixedVariables.

Returns:

The likelihood.

getPenaltyDiscount

public double getPenaltyDiscount()

setPenaltyDiscount

public void setPenaltyDiscount(double penaltyDiscount)

setDiscretize

public void setDiscretize(boolean discretize)

setNumCategoriesToDiscretize

public void setNumCategoriesToDiscretize(int numCategoriesToDiscretize)