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

Class BCCausalInference

java.lang.Object

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

public class BCCausalInference
extends Object

This is a thread-safe version of BCInference.

Jan 30, 2019 5:42:50 PM

Version:

$Id: $Id

Author:

Kevin V. Bui (kvb2@pitt.edu)

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	BCCausalInference.OP	An enum for the type of operation.

Constructor Summary

Constructors

Constructor	Description
BCCausalInference(int[] nodeDimension, int[][] cases)	Constructor

Method Summary

Modifier and Type	Method	Description
double	probConstraint(BCCausalInference.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.

Methods inherited from class java.lang.Object

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

Constructor Details

BCCausalInference

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

Constructor

Parameters:

nodeDimension - nodeDimension[0] is the number of nodes, nodeDimension[1] is the number of cases, and the rest are the dimensions of the nodes.

cases - cases[0] is the number of cases, and the rest are the cases.

Method Details

probConstraint

public double probConstraint(BCCausalInference.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] = 0 represents an empty set.

Set Z with two elements: Z = {3, 2} Z[0] = 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)