wolffd@0: % Inference on a conditional Gaussian model
wolffd@0: 
wolffd@0: % Make the following polytree, where all arcs point down
wolffd@0: 
wolffd@0: % 1   2
wolffd@0: %  \ /
wolffd@0: %   3
wolffd@0: %  / \
wolffd@0: % 4   5
wolffd@0: 
wolffd@0: N = 5;
wolffd@0: dag = zeros(N,N);
wolffd@0: dag(1,3) = 1;
wolffd@0: dag(2,3) = 1;
wolffd@0: dag(3, [4 5]) = 1;
wolffd@0: 
wolffd@0: ns = [2 1 2 1 2];
wolffd@0: 
wolffd@0: dnodes = 1;
wolffd@0: %onodes = [1 5];
wolffd@0: bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', dnodes);
wolffd@0: 
wolffd@0: bnet.CPD{1} = tabular_CPD(bnet, 1);
wolffd@0: for i=2:N
wolffd@0:   bnet.CPD{i} = gaussian_CPD(bnet, i);
wolffd@0: end
wolffd@0: 
wolffd@0: engine = {};
wolffd@0: engine{end+1} = jtree_inf_engine(bnet);
wolffd@0: engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
wolffd@0: 
wolffd@0: [time, engine] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 0, ...
wolffd@0: 				      'singletons_only', 0, 'observed', [1 3]);