Package edu.cmu.tetrad.search.score

Class DiscreteBicScoreAdTree

java.lang.Object

edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree

All Implemented Interfaces:

DiscreteScore, Score

public class DiscreteBicScoreAdTree
extends Object
implements DiscreteScore

Calculates the discrete BIC score. The likelihood for this score is calculated as SUM(ln(P(X | Z) P(Z))) across all cells in all conditional probability tables for the discrete model. The parameters are counted as SUM(rows * (cols - 1)) for all conditional probability tables in the model, where rows summing to zero are discounted, as their marginal probabilities cannot be calcualted. Then the BIC score is calculated as 2L - ck ln N, where c is a multiplier on the penalty ("penalty discount").

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

Version:

$Id: $Id

Author:

josephramsey

Constructor Summary

Constructors

Constructor	Description
DiscreteBicScoreAdTree(DataSet dataSet)	Constructs the score using a dataset.

Method Summary

Modifier and Type	Method	Description
DataSet	getDataSet()	Returns the dataset.
int	getMaxDegree()	Returns the max degree, by default 1000.
int[]	getParentValues(int[] parents, int rowIndex)	Returns an integer array containing the parent values for a given node index and row index.
int	getSampleSize()	Returns the sample size.
List<Node>	getVariables()	The variables of the score.
boolean	isEffectEdge(double bump)	Returns true iff the edge between x and y is an effect edge.
double	localScore(int node, int[] parents)	The score of a node given its parents.
double	localScoreDiff(int x, int y, int[] z)	Returns the score difference of the graph.
void	setPenaltyDiscount(double penaltyDiscount)	Sets the penalty discount, which is a multiplier on the penalty term of BIC.
void	setSamplePrior(double samplePrior)	Sets the sample prior.
void	setStructurePrior(double structurePrior)	Sets the structure prior.
void	setVariables(List<Node> variables)	Sets the variables to a new list of the same size.
String	toString()	A string representation of the score.

Methods inherited from class java.lang.Object

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

Methods inherited from interface edu.cmu.tetrad.search.score.Score

append, determines, getVariable, localScore, localScore, localScoreDiff

Constructor Details

DiscreteBicScoreAdTree

public DiscreteBicScoreAdTree(DataSet dataSet)

Constructs the score using a dataset.

Parameters:

dataSet - The discrete dataset to analyze.

Method Details

localScore

public double localScore(int node,
 int[] parents)

The score of a node given its parents.

Returns the score of the given nodes given its parents.

Specified by:

localScore in interface Score

Parameters:

node - The node.

parents - The parents.

Returns:

The score.

localScoreDiff

public double localScoreDiff(int x,
 int y,
 int[] z)

Returns the score difference of the graph.

Returns localScore(y | z, x) - localScore(y | z).

Specified by:

localScoreDiff in interface Score

Parameters:

x - A node.

y - TAhe node.

z - A set of nodes.

Returns:

The score difference.

getVariables

public List<Node> getVariables()

The variables of the score.

Returns the variables.

Specified by:

getVariables in interface Score

Returns:

This list.

setVariables

public void setVariables(List<Node> variables)

Sets the variables to a new list of the same size.

Parameters:

variables - The new list of variables.

getSampleSize

public int getSampleSize()

Returns the sample size.

Specified by:

getSampleSize in interface Score

Returns:

This size.

isEffectEdge

public boolean isEffectEdge(double bump)

Returns true iff the edge between x and y is an effect edge.

Must be called directly after the corresponding scoring call. Used in FGES.

Specified by:

isEffectEdge in interface Score

Parameters:

bump - The bump.

Returns:

True iff the edge between x and y is an effect edge.

See Also:

• Fges

getDataSet

public DataSet getDataSet()

Returns the dataset.

Returns the dataset being analyzed.

Specified by:

getDataSet in interface DiscreteScore

Returns:

Ibid.

setStructurePrior

public void setStructurePrior(double structurePrior)

Sets the structure prior.

Sets the structure prior.

Specified by:

setStructurePrior in interface DiscreteScore

Parameters:

structurePrior - Ibid.

setSamplePrior

public void setSamplePrior(double samplePrior)

Sets the sample prior.

This method is not used for this score.

Specified by:

setSamplePrior in interface DiscreteScore

Parameters:

samplePrior - Ibid.

setPenaltyDiscount

public void setPenaltyDiscount(double penaltyDiscount)

Sets the penalty discount, which is a multiplier on the penalty term of BIC.

Parameters:

penaltyDiscount - This discount.

getMaxDegree

public int getMaxDegree()

Returns the max degree, by default 1000.

Returns the maximum degree for some algorithms.

Specified by:

getMaxDegree in interface Score

Returns:

The max degree.

toString

public String toString()

A string representation of the score.

Returns a string representation of this score.

Specified by:

toString in interface Score

Overrides:

toString in class Object

Returns:

This string.

getParentValues

public int[] getParentValues(int[] parents,
 int rowIndex)

Returns an integer array containing the parent values for a given node index and row index.

Parameters:

parents - the indices of the parent nodes.

rowIndex - the index of the row in question.

Returns:

an integer array containing the parent values.