Package edu.cmu.tetrad.search.score

Class ConditionalGaussianLikelihood

java.lang.Object

edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood

public class ConditionalGaussianLikelihood
extends Object

Implements a conditional Gaussian likelihood. Please note that this likelihood will be maximal only if the continuous variables are jointly Gaussian conditional on the discrete variables; in all other cases, it will be less than maximal. The reference is here:

Andrews, B., Ramsey, J., & Cooper, G. F. (2018). Scoring Bayesian networks of mixed variables. International journal of data science and analytics, 6, 3-18.

As for all scores in Tetrad, higher scores mean more dependence, and negative scores indicate independence.

Version:

$Id: $Id

Author:

bryanandrews, josephramsey

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final class	ConditionalGaussianLikelihood.Ret	Gives return value for a conditional Gaussian likelihood, returning 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.
void	setDiscretize(boolean discretize)	Sets whether to discretize child variables to avoid integration.
void	setMinSampleSizePerCell(int minSampleSizePerCell)	Sets the minimum sample size per cell.
void	setNumCategoriesToDiscretize(int numCategoriesToDiscretize)	Sets the number of categories to use to discretize child variables to avoid integration
void	setRows(List<Integer> rows)	Sets the rows to be used in the table.

Methods inherited from class java.lang.Object

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

Constructor Details

ConditionalGaussianLikelihood

public ConditionalGaussianLikelihood(DataSet dataSet)

Constructs the score using a covariance matrix.

Parameters:

dataSet - The continuous dataset to analyze.

Method Details

setRows

public void setRows(List<Integer> rows)

Sets the rows to be used in the table. If the rows are null, the table will use all the rows in the data set. Otherwise, the table will use only the rows specified.

Parameters:

rows - the rows to be used in the table.

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.

setDiscretize

public void setDiscretize(boolean discretize)

Sets whether to discretize child variables to avoid integration. An optimization.

Parameters:

discretize - True, if so.

See Also:

• setNumCategoriesToDiscretize(int)

setNumCategoriesToDiscretize

public void setNumCategoriesToDiscretize(int numCategoriesToDiscretize)

Sets the number of categories to use to discretize child variables to avoid integration

Parameters:

numCategoriesToDiscretize - This number.

See Also:

• setDiscretize(boolean)

setMinSampleSizePerCell

public void setMinSampleSizePerCell(int minSampleSizePerCell)

Sets the minimum sample size per cell.

Parameters:

minSampleSizePerCell - The minimum sample size per cell.