public virtual double prior(params IProposition[] phi)
{
// Calculating the prior, therefore no relevant evidence
// just query over the scope of proposition phi in order
// to get a joint distribution for these
IProposition conjunct = ProbUtil.constructConjunction(phi);
IRandomVariable[] X = conjunct.getScope().ToArray();
ICategoricalDistribution d = bayesInference.Ask(X, new AssignmentProposition[0], bayesNet);
// Then calculate the probability of the propositions phi
// be seeing where they hold.
double[] probSum = new double[1];
CategoricalDistributionIterator di = new CategoricalDistributionIteraorPrior(conjunct, probSum);
d.iterateOver(di);
return(probSum[0]);
}
public ICategoricalDistribution forward(ICategoricalDistribution f1_t, ICollection <AssignmentProposition> e_tp1)
{
ICategoricalDistribution s1 = new ProbabilityTable(f1_t.getFor());
// Set up required working variables
IProposition[] props = new IProposition[s1.getFor().Size()];
int i = 0;
foreach (IRandomVariable rv in s1.getFor())
{
props[i] = new RandVar(rv.getName(), rv.getDomain());
++i;
}
IProposition Xtp1 = ProbUtil.constructConjunction(props);
AssignmentProposition[] xt = new AssignmentProposition[tToTm1StateVarMap.Size()];
IMap <IRandomVariable, AssignmentProposition> xtVarAssignMap = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, AssignmentProposition>();
i = 0;
foreach (IRandomVariable rv in tToTm1StateVarMap.GetKeys())
{
xt[i] = new AssignmentProposition(tToTm1StateVarMap.Get(rv), "<Dummy Value>");
xtVarAssignMap.Put(rv, xt[i]);
++i;
}
// Step 1: Calculate the 1 time step prediction
// ∑<sub>x<sub>t</sub></sub>
CategoricalDistributionIterator if1_t = new CategoricalDistributionIteratorImpl(transitionModel,
xtVarAssignMap, s1, Xtp1, xt);
f1_t.iterateOver(if1_t);
// Step 2: multiply by the probability of the evidence
// and normalize
// <b>P</b>(e<sub>t+1</sub> | X<sub>t+1</sub>)
ICategoricalDistribution s2 = sensorModel.posteriorDistribution(ProbUtil
.constructConjunction(e_tp1.ToArray()), Xtp1);
return(s2.multiplyBy(s1).normalize());
}
public ICategoricalDistribution backward(ICategoricalDistribution b_kp2t, ICollection <AssignmentProposition> e_kp1)
{
ICategoricalDistribution b_kp1t = new ProbabilityTable(b_kp2t.getFor());
// Set up required working variables
IProposition[] props = new IProposition[b_kp1t.getFor().Size()];
int i = 0;
foreach (IRandomVariable rv in b_kp1t.getFor())
{
IRandomVariable prv = tToTm1StateVarMap.Get(rv);
props[i] = new RandVar(prv.getName(), prv.getDomain());
++i;
}
IProposition Xk = ProbUtil.constructConjunction(props);
AssignmentProposition[] ax_kp1 = new AssignmentProposition[tToTm1StateVarMap.Size()];
IMap <IRandomVariable, AssignmentProposition> x_kp1VarAssignMap = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, AssignmentProposition>();
i = 0;
foreach (IRandomVariable rv in b_kp1t.getFor())
{
ax_kp1[i] = new AssignmentProposition(rv, "<Dummy Value>");
x_kp1VarAssignMap.Put(rv, ax_kp1[i]);
++i;
}
IProposition x_kp1 = ProbUtil.constructConjunction(ax_kp1);
props = e_kp1.ToArray();
IProposition pe_kp1 = ProbUtil.constructConjunction(props);
// ∑<sub>x<sub>k+1</sub></sub>
CategoricalDistributionIterator ib_kp2t = new CategoricalDistributionIteratorImpl2(x_kp1VarAssignMap,
sensorModel, transitionModel, b_kp1t, pe_kp1, Xk, x_kp1);
b_kp2t.iterateOver(ib_kp2t);
return(b_kp1t);
}