Package edu.pitt.dbmi.algo.bayesian.constraint.inference

Class BCInference

java.lang.Object

edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference

public class BCInference
extends Object

Feb 26, 2014 8:07:20 PM

Version:

$Id: $Id

Author:

Kevin V. Bui (kvb2@pitt.edu)

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	BCInference.OP	Enum for the type of constraint.

Constructor Summary

Constructors

Constructor	Description
BCInference(int[][] cases, int[] nodeDimension)	Cases is a two-dimensional array dataset.

Method Summary

Modifier and Type	Method	Description
double	probConstraint(BCInference.OP constraint, int x, int y, int[] z)	This function takes a constraint, which has a value of either OP.dependent or OP.independent, of the form "X independent Y given Z" or "X dependent Y given Z" and returns a probability for that constraint given the data in cases and assumed prior probability for that constraint given the data in cases and assumed prior probabilities.
void	setPriorEqivalentSampleSize(double priorEquivalentSampleSize)	Sets the prior equivalent sample size.

Methods inherited from class java.lang.Object

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

Constructor Details

BCInference

public BCInference(int[][] cases,
 int[] nodeDimension)

Cases is a two-dimensional array dataset. If the dataset is M x N, the size of the two-dimensional array is (M + 2) x (N + 2). In other words, the size of the array is always 2 more of the number of data. Likewise, if the data for nodeDimension is N then the size of the array is N + 2.

The case array index starts from 1 (not zero) to numberOfCases. The nodeDimension array index start from 1 (not zero) to numberOfNodes.

nodeDimension array contains values denote the number of discrete values that Node can have (e.g., 2 for a binary variable).

Parameters:

cases - is a two-dimensional integer array containing the data

nodeDimension - one-dimensional integer array containing the dimension of each variable

Method Details

probConstraint

public double probConstraint(BCInference.OP constraint,
 int x,
 int y,
 int[] z)

This function takes a constraint, which has a value of either OP.dependent or OP.independent, of the form "X independent Y given Z" or "X dependent Y given Z" and returns a probability for that constraint given the data in cases and assumed prior probability for that constraint given the data in cases and assumed prior probabilities. Currently, it assumes uniform parameter priors and a structure prior of 0.5. A structure prior of 0.5 means taht a priori we have that P(X independent Y given Z) = P(X dependent Y given Z) = 0.5.

Z[0] is the length of the set represented by array Z. For an example, Z[0] = 1 represents the set Z of size 1. Z[] = 0 represents an empty set.

Set Z with two elements: Z = {3, 2} Z[] = 2 // set Z has two elements (length of 2) Z[1] = 3 // first element Z[2] = 2 // second element.

Empty set: Z = {}; Z[0] = 0

Parameters:

constraint - has the value OP.independent or OP.dependent

x - node x

y - node y

z - set of nodes

Returns:

P(x dependent y given z | data) or P(x independent y given z | data)

setPriorEqivalentSampleSize

public void setPriorEqivalentSampleSize(double priorEquivalentSampleSize)

Sets the prior equivalent sample size.

Parameters:

priorEquivalentSampleSize - the prior equivalent sample size