Daniel@0: % Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17)
Daniel@0: %    1
Daniel@0: %  / | \
Daniel@0: % 2  3  4
Daniel@0: % |  |  |
Daniel@0: % 5  6  7
Daniel@0: %  \/ \/
Daniel@0: %  8   9
Daniel@0: % where all arcs point downwards
Daniel@0: 
Daniel@0: N = 9;
Daniel@0: dag = zeros(N,N);
Daniel@0: dag(1,2)=1; dag(1,3)=1; dag(1,4)=1;
Daniel@0: dag(2,5)=1; dag(3,6)=1; dag(4,7)=1;
Daniel@0: dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1;
Daniel@0: 
Daniel@0: gauss = 1;
Daniel@0: if gauss
Daniel@0:   ns = ones(1,N); % scalar nodes
Daniel@0:   ns(1) = 2;
Daniel@0:   ns(9) = 3;
Daniel@0:   dnodes = [];
Daniel@0: else
Daniel@0:   ns = 2*ones(1,N); % binary nodes
Daniel@0:   dnodes = 1:N;
Daniel@0: end
Daniel@0: 
Daniel@0: bnet = mk_bnet(dag, ns, 'discrete', dnodes);
Daniel@0: % use random params
Daniel@0: for i=1:N
Daniel@0:   if gauss
Daniel@0:     bnet.CPD{i} = gaussian_CPD(bnet, i);
Daniel@0:   else
Daniel@0:     bnet.CPD{i} = tabular_CPD(bnet, i);
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: engines = {};
Daniel@0: engines{1} = jtree_inf_engine(bnet);
Daniel@0: engines{2} = stab_cond_gauss_inf_engine(bnet);
Daniel@0: 
Daniel@0: [err, time] = cmp_inference_static(bnet, engines);