Package edu.cmu.tetrad.search.score

Class MvpScore

java.lang.Object

edu.cmu.tetrad.search.score.MvpScore

All Implemented Interfaces:

Score

public class MvpScore
extends Object
implements Score

Implements a mixed variable polynomial BIC score. 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.

Version:

$Id: $Id

Author:

Bryan Andrews

Constructor Summary

Constructors

Constructor	Description
MvpScore(DataSet dataSet, double structurePrior, int fDegree, boolean discretize, int effectiveSampleSize)	Constructs an MvpScore instance with the given dataset and parameters.

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.
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)	The local score of the child given its parents.
double	localScoreDiff(int x, int y, int[] z)	Returns localScore(y | z, x) - localScore(y | z).

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

MvpScore

public MvpScore(DataSet dataSet,
 double structurePrior,
 int fDegree,
 boolean discretize,
 int effectiveSampleSize)

Constructs an MvpScore instance with the given dataset and parameters.

Parameters:

dataSet - The dataset to be used for scoring. Must not be null.

structurePrior - The prior over structures, influencing the scoring model.

fDegree - The degree of freedom adjustment for the scoring algorithm.

discretize - Whether the data should be discretized (true) or not (false).

effectiveSampleSize - The effective sample size to be used; if less than 0, the actual sample size of the dataset is used.

Throws:

NullPointerException - If the dataSet is null.

Method Details

localScore

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

The local score of the child given its parents.

Specified by:

localScore in interface Score

Parameters:

i - The child.

parents - The parents.

Returns:

The local score.

localScoreDiff

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

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

Specified by:

localScoreDiff in interface Score

Parameters:

x - A node.

y - The node.

z - A set of nodes.

Returns:

The score difference.

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.

A method for FGES returning a judgment of whether an edge with a given bump counts as a 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.

See Also:

• Fges

getVariables

public List<Node> getVariables()

The variables of the score.

Returns the list of variables.

Specified by:

getVariables in interface Score

Returns:

This list.

getMaxDegree

public int getMaxDegree()

Returns the max degree, by default 1000.

Returns an estimate of the maximum degree of the graph for some algorithms.

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 a judgment of whether the variable in z determine y exactly.

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.