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.

Version:

$Id: $Id

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)	Returns true iff the score determines the edge between x and y.
ICovarianceMatrix	getCovariances()	Returns the covariance matrix.
DataModel	getData()	Returns the data set.
int	getMaxDegree()	Returns the max degree, by default 1000.
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 i, int... parents)	Returns the score of the node at index i, given its parents.
double	localScoreDiff(int x, int y, int[] z)	Returns the score difference of the graph.
void	setLambda(double lambda)	Sets the lambda parameter.
void	setUsePseudoInverse(boolean usePseudoInverse)	Sets whether the pseudo-inverse should be used.

Methods inherited from class java.lang.Object

equals, 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.

Parameters:

covariances - The covariance matrix.

PoissonPriorScore

public PoissonPriorScore(DataSet dataSet,
 boolean precomputeCovariances)

Constructs the score using a covariance matrix.

Parameters:

dataSet - The dataset.

precomputeCovariances - Whether the covariances should be precomputed or computed on the fly. True if

Method Details

localScoreDiff

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

Returns the score difference of the graph.

Returns the score difference 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.

localScore

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

Returns the score of the node at index i, 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 - if any.

getCovariances

public ICovarianceMatrix getCovariances()

Returns the covariance matrix.

Returns:

The covariance matrix.

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.

Returns true if the edge with the given bump is an effect edge.

Specified by:

isEffectEdge in interface Score

Parameters:

bump - The bump.

Returns:

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

getVariables

public List<Node> getVariables()

The variables of the score.

Returns the variables.

Specified by:

getVariables in interface Score

Returns:

This list.

getMaxDegree

public int getMaxDegree()

Returns the max degree, by default 1000.

Returns the max degree.

Specified by:

getMaxDegree in interface Score

Returns:

The max degree.

determines

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

Returns true iff the score determines the edge between x and y.

Returns true if z determines y.

Specified by:

determines in interface Score

Parameters:

z - The set of nodes.

y - The node.

Returns:

True iff the score determines the edge between x and y.

getData

public DataModel getData()

Returns the data set.

Returns:

The data set.

setLambda

public void setLambda(double lambda)

Sets the lambda parameter.

Parameters:

lambda - The lambda parameter.

setUsePseudoInverse

public void setUsePseudoInverse(boolean usePseudoInverse)

Sets whether the pseudo-inverse should be used.

Parameters:

usePseudoInverse - True if the pseudo-inverse should be used.