// function GIBBS-ASK(X, e, bn, N) returns an estimate of <b>P</b>(X|e)
/**
* The GIBBS-ASK algorithm in Figure 14.16. For answering queries given
* evidence in a Bayesian Network.
*
* @param X
* the query variables
* @param e
* observed values for variables E
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @param Nsamples
* the total number of samples to be generated
* @return an estimate of <b>P</b>(X|e)
*/
public CategoricalDistribution gibbsAsk(RandomVariable[] X,
AssignmentProposition[] e, BayesianNetwork bn, int Nsamples)
{
// local variables: <b>N</b>, a vector of counts for each value of X,
// initially zero
double[] N = new double[ProbUtil
.expectedSizeOfCategoricalDistribution(X)];
// Z, the nonevidence variables in bn
Set <RandomVariable> Z = new Set <RandomVariable>(
bn.getVariablesInTopologicalOrder());
foreach (AssignmentProposition ap in e)
{
Z.Remove(ap.getTermVariable());
}
// <b>x</b>, the current state of the network, initially copied from e
Map <RandomVariable, Object> x = new LinkedHashMap <RandomVariable, Object>();
foreach (AssignmentProposition ap in e)
{
x.Add(ap.getTermVariable(), ap.getValue());
}
// initialize <b>x</b> with random values for the variables in Z
foreach (RandomVariable Zi in
Z)
{
x.put(Zi, ProbUtil.randomSample(bn.getNode(Zi), x, randomizer));
}
// for j = 1 to N do
for (int j = 0; j < Nsamples; j++)
{
// for each Z<sub>i</sub> in Z do
foreach (RandomVariable Zi in
Z)
{
// set the value of Z<sub>i</sub> in <b>x</b> by sampling from
// <b>P</b>(Z<sub>i</sub>|mb(Z<sub>i</sub>))
x.put(Zi,
ProbUtil.mbRandomSample(bn.getNode(Zi), x, randomizer));
}
// Note: moving this outside the previous for loop,
// as described in fig 14.6, as will only work
// correctly in the case of a single query variable X.
// However, when multiple query variables, rare events
// will get weighted incorrectly if done above. In case
// of single variable this does not happen as each possible
// value gets * |Z| above, ending up with the same ratios
// when normalized (i.e. its still more efficient to place
// outside the loop).
//
// <b>N</b>[x] <- <b>N</b>[x] + 1
// where x is the value of X in <b>x</b>
N[ProbUtil.indexOf(X, x)] += 1.0;
}
// return NORMALIZE(<b>N</b>)
return(new ProbabilityTable(N, X).normalize());
}
// function LIKELIHOOD-WEIGHTING(X, e, bn, N) returns an estimate of
// <b>P</b>(X|e)
/**
* The LIKELIHOOD-WEIGHTING algorithm in Figure 14.15. For answering queries
* given evidence in a Bayesian Network.
*
* @param X
* the query variables
* @param e
* observed values for variables E
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @param N
* the total number of samples to be generated
* @return an estimate of <b>P</b>(X|e)
*/
public ICategoricalDistribution likelihoodWeighting(IRandomVariable[] X, AssignmentProposition[] e, IBayesianNetwork bn, int N)
{
// local variables: W, a vector of weighted counts for each value of X,
// initially zero
double[] W = new double[ProbUtil.expectedSizeOfCategoricalDistribution(X)];
// for j = 1 to N do
for (int j = 0; j < N; j++)
{
// <b>x</b>,w <- WEIGHTED-SAMPLE(bn,e)
Pair <IMap <IRandomVariable, object>, double> x_w = weightedSample(bn, e);
// W[x] <- W[x] + w where x is the value of X in <b>x</b>
W[ProbUtil.indexOf(X, x_w.GetFirst())] += x_w.getSecond();
}
// return NORMALIZE(W)
return(new ProbabilityTable(W, X).normalize());
}
// function REJECTION-SAMPLING(X, e, bn, N) returns an estimate of
// <b>P</b>(X|e)
/**
* The REJECTION-SAMPLING algorithm in Figure 14.14. For answering queries
* given evidence in a Bayesian Network.
*
* @param X
* the query variables
* @param e
* observed values for variables E
* @param bn
* a Bayesian network
* @param Nsamples
* the total number of samples to be generated
* @return an estimate of <b>P</b>(X|e)
*/
public ICategoricalDistribution rejectionSampling(IRandomVariable[] X, AssignmentProposition[] e, IBayesianNetwork bn, int Nsamples)
{
// local variables: <b>N</b>, a vector of counts for each value of X,
// initially zero
double[] N = new double[ProbUtil.expectedSizeOfCategoricalDistribution(X)];
// for j = 1 to N do
for (int j = 0; j < Nsamples; j++)
{
// <b>x</b> <- PRIOR-SAMPLE(bn)
IMap <IRandomVariable, object> x = ps.priorSample(bn);
// if <b>x</b> is consistent with e then
if (isConsistent(x, e))
{
// <b>N</b>[x] <- <b>N</b>[x] + 1
// where x is the value of X in <b>x</b>
N[ProbUtil.indexOf(X, x)] += 1.0;
}
}
// return NORMALIZE(<b>N</b>)
return(new ProbabilityTable(N, X).normalize());
}