public virtual ICategoricalDistribution posteriorDistribution(IProposition phi, params IProposition[] evidence)
{
IProposition conjEvidence = ProbUtil.constructConjunction(evidence);
// P(A | B) = P(A AND B)/P(B) - (13.3 AIMA3e)
ICategoricalDistribution dAandB = jointDistribution(phi, conjEvidence);
ICategoricalDistribution dEvidence = jointDistribution(conjEvidence);
ICategoricalDistribution rVal = dAandB.divideBy(dEvidence);
// Note: Need to ensure normalize() is called
// in order to handle the case where an approximate
// algorithm is used (i.e. won't evenly divide
// as will have calculated on separate approximate
// runs). However, this should only be done
// if the all of the evidences scope are bound (if not
// you are returning in essence a set of conditional
// distributions, which you do not want normalized).
bool unboundEvidence = false;
foreach (IProposition e in evidence)
{
if (e.getUnboundScope().Size() > 0)
{
unboundEvidence = true;
break;
}
}
if (!unboundEvidence)
{
rVal.normalize();
}
return(rVal);
}
public ICategoricalDistribution posteriorDistribution(IProposition phi,
params IProposition[] evidence)
{
IProposition conjEvidence = ProbUtil.constructConjunction(evidence);
// P(A | B) = P(A AND B)/P(B) - (13.3 AIMA3e)
ICategoricalDistribution dAandB = jointDistribution(phi, conjEvidence);
ICategoricalDistribution dEvidence = jointDistribution(conjEvidence);
return(dAandB.divideBy(dEvidence));
}