wolffd@0: function [mpe, prob] = calc_mpe_given_inf_engine(engine, evidence)
wolffd@0: % CALC_MPE_GIVEN_ENGINE Computes the most probable explanation of the evidence
wolffd@0: % [mpe, prob] = calc_mpe_given_inf_engine(engine, evidence)
wolffd@0: %
wolffd@0: % INPUT
wolffd@0: % engine must support max-propagation
wolffd@0: % evidence{i} is the obsevred value of node i, or [] if hidden
wolffd@0: %
wolffd@0: % OUTPUT
wolffd@0: % mpe(i) is the most likely value of node i
wolffd@0: % prob is the likelihood of the globally best assignment
wolffd@0: %
wolffd@0: % This currently only works when all nodes are discrete
wolffd@0: 
wolffd@0: [engine, ll] = enter_evidence(engine, evidence);
wolffd@0: 
wolffd@0: observed = ~isemptycell(evidence);
wolffd@0: N = length(evidence);
wolffd@0: mpe = zeros(1,N);
wolffd@0: for i=1:N
wolffd@0:   m = marginal_nodes(engine, i);
wolffd@0:   % discrete observed nodes are all set to 1 inside the inference engine, so we must undo this
wolffd@0:   if observed(i)
wolffd@0:     mpe(i) = evidence{i};
wolffd@0:   else
wolffd@0:     mpe(i) = argmax(m.T);
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: bnet = bnet_from_engine(engine);
wolffd@0: ll = log_lik_complete(bnet, num2cell(mpe(:)));
wolffd@0: prob = exp(ll);