wolffd@0: N = 1; % regular HMM
wolffd@0: Q = 2;
wolffd@0: ss = 2;
wolffd@0: hnodes = 1;
wolffd@0: onodes = 2;
wolffd@0: 
wolffd@0: rand('state', 0);
wolffd@0: randn('state', 0);
wolffd@0: O = 2;
wolffd@0: discrete_obs = 1;
wolffd@0: bnet = mk_chmm(N, Q, O, discrete_obs);
wolffd@0: ns = bnet.node_sizes_slice;
wolffd@0: 
wolffd@0: engine = hmm_inf_engine(bnet, onodes);
wolffd@0: 
wolffd@0: T = 4;
wolffd@0: ev = cell(ss,T);
wolffd@0: ev(onodes,:) = num2cell(sample_discrete([0.5 0.5], N, T));
wolffd@0: 
wolffd@0: 
wolffd@0: engine = dbn_init_bel(engine);
wolffd@0: for t=1:T
wolffd@0:   if t==1
wolffd@0:     [engine, ll(t)] = dbn_update_bel1(engine, ev(:,t));
wolffd@0:   else
wolffd@0:     [engine, ll(t)] = dbn_update_bel(engine, ev(:,t-1:t));
wolffd@0:   end
wolffd@0:   % one-step ahead prediction
wolffd@0:   lag = 1;
wolffd@0:   engine2 = dbn_predict_bel(engine, lag);  
wolffd@0:   marg = dbn_marginal_from_bel(engine2, 1)
wolffd@0:   marg = dbn_marginal_from_bel(engine2, 2)
wolffd@0: end