Daniel@0: function L = log_prob_node(CPD, self_ev, pev)
Daniel@0: % LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian)
Daniel@0: % L = log_prob_node(CPD, self_ev, pev)
Daniel@0: %
Daniel@0: % self_ev(m) is the evidence on this node in case m.
Daniel@0: % pev(i,m) is the evidence on the i'th parent in case m (if there are any parents).
Daniel@0: % (These may also be cell arrays.)
Daniel@0: 
Daniel@0: if iscell(self_ev), usecell = 1; else usecell = 0; end
Daniel@0: 
Daniel@0: use_log = 1;
Daniel@0: ncases = length(self_ev);
Daniel@0: nparents = length(CPD.sizes)-1;
Daniel@0: assert(ncases == size(pev, 2));
Daniel@0: 
Daniel@0: if ncases == 0
Daniel@0:   L = 0;
Daniel@0:   return;
Daniel@0: end
Daniel@0: 
Daniel@0: L = 0;
Daniel@0: for m=1:ncases
Daniel@0:   if isempty(CPD.dps)
Daniel@0:     i = 1;
Daniel@0:   else
Daniel@0:     if usecell
Daniel@0:       dpvals = cat(1, pev{CPD.dps, m});
Daniel@0:     else
Daniel@0:       dpvals = pev(CPD.dps, m);
Daniel@0:     end
Daniel@0:     i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)');
Daniel@0:   end
Daniel@0:   if usecell
Daniel@0:     y = self_ev{m};
Daniel@0:   else
Daniel@0:     y = self_ev(m);
Daniel@0:   end
Daniel@0:   if length(CPD.cps) == 0 
Daniel@0:     L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);
Daniel@0:   else
Daniel@0:     if usecell
Daniel@0:       x = cat(1, pev{CPD.cps, m});
Daniel@0:     else
Daniel@0:       x = pev(CPD.cps, m);
Daniel@0:     end
Daniel@0:     L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log);
Daniel@0:   end
Daniel@0: end
Daniel@0: