wolffd@0: function obslik = mk_hmm_obs_lik_matrix(engine, evidence)
wolffd@0: 
wolffd@0: T  = size(evidence,2);
wolffd@0: Q = length(engine.startprob);
wolffd@0: obslik = ones(Q, T);
wolffd@0: bnet = bnet_from_engine(engine);
wolffd@0: % P(o1,o2| Q1,Q2) = P(o1|Q1,Q2) * P(o2|Q1,Q2)
wolffd@0: onodes = bnet.observed;
wolffd@0: for i=1:length(onodes)
wolffd@0:   data = cell2num(evidence(onodes(i),:));
wolffd@0:   if bnet.auto_regressive(onodes(i))
wolffd@0:     params = engine.obsprob{i};
wolffd@0:     mu = params.big_mu;
wolffd@0:     Sigma = params.big_Sigma,
wolffd@0:     W = params.big_W;
wolffd@0:     mu0 = params.big_mu0;
wolffd@0:     Sigma0 = params.big_Sigma0;
wolffd@0:     %obslik_i = mk_arhmm_obs_lik(data, mu, Sigma, W, mu0, Sigma0
wolffd@0:     obslik_i = clg_prob(data(:,1:T-1), data(:,2:T), mu, Sigma, W);
wolffd@0:     obslik_i = [mixgauss_prob(data(:,1), mu0, Sigma0) obslik_i];
wolffd@0:   elseif myismember(onodes(i), bnet.dnodes)
wolffd@0:     %obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.big_CPT);
wolffd@0:     obslik_i = multinomial_prob(data, engine.obsprob{i}.big_CPT);
wolffd@0:   else
wolffd@0:     %obslik_i = eval_pdf_cond_gauss(data, engine.obsprob{i}.big_mu, engine.obsprob{i}.big_Sigma);
wolffd@0:     obslik_i = mixgauss_prob(data, engine.obsprob{i}.big_mu, engine.obsprob{i}.big_Sigma);
wolffd@0:   end
wolffd@0:   obslik = obslik .* obslik_i;
wolffd@0: end
wolffd@0: