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