wolffd@0: function L = log_marg_prob_node_case(CPD, y, x)
wolffd@0: % LOG_MARG_PROB_NODE_CASE Compute prod_m log P(x(i,m)| x(pi_i,m)) for node i (tabular)
wolffd@0: % L = log_marg_prob_node_case(CPD, self_ev, parent_ev)
wolffd@0: % 
wolffd@0: % This is a slightly optimised version of log_marg_prob_node.
wolffd@0: % We assume we have exactly 1 case, i.e., y is a scalar and x is a vector (not a cell array).
wolffd@0: 
wolffd@0: sz = CPD.sizes;
wolffd@0: nparents = length(sz)-1;
wolffd@0: 
wolffd@0: % We assume the CPTs are already set to the mean of the posterior (due to update_params)
wolffd@0: 
wolffd@0: switch nparents
wolffd@0:  case 0, p = CPD.CPT(y);
wolffd@0:  case 1, p = CPD.CPT(x(1), y);
wolffd@0:  case 2, p = CPD.CPT(x(1), x(2), y);
wolffd@0:  case 3, p = CPD.CPT(x(1), x(2), x(3), y);
wolffd@0:  otherwise,
wolffd@0:   ind = subv2ind(sz, [x y]);
wolffd@0:   p = CPD.CPT(ind);
wolffd@0: end
wolffd@0: L = log(p);