aima.core.probability.bayes.approx

Class GibbsAsk

java.lang.Object

aima.core.probability.bayes.approx.GibbsAsk

All Implemented Interfaces:

BayesSampleInference

public class GibbsAsk
extends java.lang.Object
implements BayesSampleInference

Artificial Intelligence A Modern Approach (3rd Edition): page 537.

 function GIBBS-ASK(X, e, bn, N) returns an estimate of P(X|e)
   local variables: N, a vector of counts for each value of X, initially zero
                    Z, the nonevidence variables in bn
                    x, the current state of the network, initially copied from e
                    
   initialize x with random values for the variables in Z
   for j = 1 to N do
       for each Zi in Z do
           set the value of Zi in x by sampling from P(Zi|mb(Zi))
           N[x] <- N[x] + 1 where x is the value of X in x
   return NORMALIZE(N)
 

Figure 14.16 The Gibbs sampling algorithm for approximate inference in Bayesian networks; this version cycles through the variables, but choosing variables at random also works.

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

Author:

Ciaran O'Reilly, Ravi Mohan

Constructor Summary

Constructors

Constructor and Description
GibbsAsk()
GibbsAsk(Randomizer r)

Method Summary

Modifier and Type	Method and Description
CategoricalDistribution	ask(RandomVariable[] X, AssignmentProposition[] observedEvidence, BayesianNetwork bn, int N)
CategoricalDistribution	gibbsAsk(RandomVariable[] X, AssignmentProposition[] e, BayesianNetwork bn, int Nsamples) The GIBBS-ASK algorithm in Figure 14.16.

Methods inherited from class java.lang.Object

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

Constructor Detail

GibbsAsk

public GibbsAsk()

GibbsAsk

public GibbsAsk(Randomizer r)

Method Detail

gibbsAsk

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

The GIBBS-ASK algorithm in Figure 14.16. For answering queries given evidence in a Bayesian Network.

Parameters:

X - the query variables

e - observed values for variables E

bn - a Bayesian network specifying joint distribution P(X₁,...,X_n)

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).