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

Author:
Kevin V. Bui (kvb2@pitt.edu)

  • Nested Class Summary

    Nested Classes
    Modifier and Type
    Class
    Description
    static enum 
    BCCausalInference.OP
     

  • Constructor Summary

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

  • 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

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

  • Constructor Details

    • BCCausalInference

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