aima.core.probability.bayes.approx

Class RejectionSampling

java.lang.Object

aima.core.probability.bayes.approx.RejectionSampling

All Implemented Interfaces:

BayesSampleInference

public class RejectionSampling
extends java.lang.Object
implements BayesSampleInference

Artificial Intelligence A Modern Approach (3rd Edition): page 533.

 function REJECTION-SAMPLING(X, e, bn, N) returns an estimate of P(X|e)
   inputs: X, the query variable
           e, observed values for variables E
           bn, a Bayesian network
           N, the total number of samples to be generated
   local variables: N, a vector of counts for each value of X, initially zero
   
   for j = 1 to N do
       x <- PRIOR-SAMPLE(bn)
       if x is consistent with e then
          N[x] <- N[x] + 1 where x is the value of X in x
   return NORMALIZE(N)
 

Figure 14.14 The rejection-sampling algorithm for answering queries given evidence in a Bayesian Network.

Note: The implementation has been extended to handle queries with multiple variables.

Author:

Ciaran O'Reilly, Ravi Mohan

Constructor Summary

Constructors

Constructor and Description
RejectionSampling()
RejectionSampling(PriorSample ps)

Method Summary

Modifier and Type	Method and Description
CategoricalDistribution	ask(RandomVariable[] X, AssignmentProposition[] observedEvidence, BayesianNetwork bn, int N)
CategoricalDistribution	rejectionSampling(RandomVariable[] X, AssignmentProposition[] e, BayesianNetwork bn, int Nsamples) The REJECTION-SAMPLING algorithm in Figure 14.14.

Methods inherited from class java.lang.Object

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

Constructor Detail

RejectionSampling

public RejectionSampling()

RejectionSampling

public RejectionSampling(PriorSample ps)

Method Detail

rejectionSampling

public CategoricalDistribution rejectionSampling(RandomVariable[] X,
                                                 AssignmentProposition[] e,
                                                 BayesianNetwork bn,
                                                 int Nsamples)

The REJECTION-SAMPLING algorithm in Figure 14.14. For answering queries given evidence in a Bayesian Network.

Parameters:

X - the query variables

e - observed values for variables E

bn - a Bayesian network

Nsamples - the total number of samples to be generated

Returns:

an estimate of P(X|e)

ask

public CategoricalDistribution ask(RandomVariable[] X,
                                   AssignmentProposition[] observedEvidence,
                                   BayesianNetwork bn,
                                   int N)

Specified by:

ask in interface BayesSampleInference

Parameters:

X - the query variables.

observedEvidence - observed values for variables E.

bn - a Bayes net with variables {X} ∪ E ∪ Y /* Y = hidden variables

N - the total number of samples to be generated

Returns:

an estimate of P(X|e).