wolffd@0: % Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17)
wolffd@0: %    1
wolffd@0: %  / | \
wolffd@0: % 2  3  4
wolffd@0: % |  |  |
wolffd@0: % 5  6  7
wolffd@0: %  \/ \/
wolffd@0: %  8   9
wolffd@0: % where all arcs point downwards
wolffd@0: seed = 0;
wolffd@0: rand('state', seed);
wolffd@0: randn('state', seed);
wolffd@0: 
wolffd@0: N = 9;
wolffd@0: dag = zeros(N,N);
wolffd@0: dag(1,2)=1; dag(1,3)=1; dag(1,4)=1;
wolffd@0: dag(2,5)=1; dag(3,6)=1; dag(4,7)=1;
wolffd@0: dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1;
wolffd@0: 
wolffd@0: dnodes = 1:N;
wolffd@0: false = 1; true = 2;
wolffd@0: ns = 2*ones(1,N); % binary nodes
wolffd@0: 
wolffd@0: onodes = [2 4];
wolffd@0: bnet = mk_bnet(dag, ns, 'observed', onodes);
wolffd@0: % use random params
wolffd@0: for i=1:N
wolffd@0:   bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0: end
wolffd@0: 
wolffd@0: %USEC = exist('@jtree_C_inf_engine/collect_evidence','file');
wolffd@0: query = [3];
wolffd@0: engine = {};
wolffd@0: engine{end+1} = jtree_inf_engine(bnet);
wolffd@0: engine{end+1} = jtree_sparse_inf_engine(bnet);
wolffd@0: %engine{end+1} = jtree_ndx_inf_engine(bnet, 'ndx_type', 'SD');
wolffd@0: %engine{end+1} = jtree_ndx_inf_engine(bnet, 'ndx_type', 'B');
wolffd@0: %engine{end+1} = jtree_ndx_inf_engine(bnet, 'ndx_type', 'D');
wolffd@0: %if USEC, engine{end+1} = jtree_C_inf_engine(bnet); end
wolffd@0: %engine{end+1} = var_elim_inf_engine(bnet);
wolffd@0: %engine{end+1} = enumerative_inf_engine(bnet);
wolffd@0: %engine{end+1} = jtree_onepass_inf_engine(bnet, query, onodes);
wolffd@0: 
wolffd@0: maximize = 0;  % jtree_ndx crashes on max-prop
wolffd@0: [err, time] = cmp_inference_static(bnet, engine, 'maximize', maximize);
wolffd@0: