Package edu.cmu.tetrad.search.score

Class PoissonPriorScore

java.lang.Object

edu.cmu.tetrad.search.score.PoissonPriorScore

All Implemented Interfaces:

Score

public class PoissonPriorScore
extends Object
implements Score

Implements Poisson prior score, a novel (unpubished) score that replaces the penalty term in BIC by the log of the Poisson distribution. The Poisson distribution has a lambda parameter, which is made a parameter of this score and acts like a structure prior for the score.

Here is the Wikipedia page for the Poisson distribution, for reference:

https://en.wikipedia.org/wiki/Poisson_distribution

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

Author:

bryanandrews, josephramsey

Constructor Summary

Constructors

Constructor	Description
PoissonPriorScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
PoissonPriorScore(ICovarianceMatrix covariances)	Constructs the score using a covariance matrix.

Method Summary

Modifier and Type	Method	Description
boolean	determines(List<Node> z, Node y)
ICovarianceMatrix	getCovariances()
DataModel	getData()
int	getMaxDegree()
int	getSampleSize()	The sample size of the data.
List<Node>	getVariables()	The variables of the score.
boolean	isEffectEdge(double bump)
double	localScore(int i, int... parents)	The score of a node given its parents.
double	localScoreDiff(int x, int y, int[] z)
void	setLambda(double lambda)
void	setVerbose(boolean verbose)

Methods inherited from class java.lang.Object

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

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

append, getVariable, localScore, localScore, localScoreDiff, toString

Constructor Details

PoissonPriorScore

public PoissonPriorScore(ICovarianceMatrix covariances)

Constructs the score using a covariance matrix.

PoissonPriorScore

public PoissonPriorScore(DataSet dataSet,
 boolean precomputeCovariances)

Constructs the score using a covariance matrix.

Method Details

localScoreDiff

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

Specified by:

localScoreDiff in interface Score

localScore

public double localScore(int i,
 int... parents)
                  throws RuntimeException

Description copied from interface: Score

The score of a node given its parents.

Specified by:

localScore in interface Score

Parameters:

i - The index of the node.

parents - The indices of the node's parents.

Returns:

The score, or NaN if the score cannot be calculated.

Throws:

RuntimeException

getCovariances

public ICovarianceMatrix getCovariances()

getSampleSize

public int getSampleSize()

Description copied from interface: Score

The sample size of the data.

Specified by:

getSampleSize in interface Score

Returns:

This size.

isEffectEdge

public boolean isEffectEdge(double bump)

Specified by:

isEffectEdge in interface Score

setVerbose

public void setVerbose(boolean verbose)

getVariables

public List<Node> getVariables()

Description copied from interface: Score

The variables of the score.

Specified by:

getVariables in interface Score

Returns:

This list.

getMaxDegree

public int getMaxDegree()

Specified by:

getMaxDegree in interface Score

determines

public boolean determines(List<Node> z,
 Node y)

Specified by:

determines in interface Score

getData

public DataModel getData()

setLambda

public void setLambda(double lambda)