% This example is from Page.143 of "Probabilistic Networks and Expert Systems",% Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer.X = 1; Y = 2; Z = 3;n = 3;dag = zeros(n);dag(X, Y)=1;dag(Y, Z)=1;ns = ones(1, n);dnodes = [];bnet = mk_bnet(dag, ns, dnodes);bnet.CPD{X} = gaussian_CPD(bnet, X, 'mean', 0, 'cov', 1);bnet.CPD{Y} = gaussian_CPD(bnet, Y, 'mean', 0, 'cov', 1, 'weights', 1);bnet.CPD{Z} = gaussian_CPD(bnet, Z, 'mean', 0, 'cov', 1, 'weights', 1);engines = {};engines{end+1} = jtree_inf_engine(bnet);engines{end+1} = stab_cond_gauss_inf_engine(bnet);nengines = length(engines);evidence = cell(1,n);evidence{Y} = 1.5; for e=1:nengines  engines{e} = enter_evidence(engines{e}, evidence);  margX = marginal_nodes(engines{e}, X);  assert(approxeq(margX.mu, 0.75))  assert(approxeq(margX.Sigma, 0.5))    margZ = marginal_nodes(engines{e}, Z);  assert(approxeq(margZ.mu, 1.5))  assert(approxeq(margZ.Sigma, 1))endevidence = cell(1,n);evidence{Z} = 1.5; for e=1:nengines  engines{e} = enter_evidence(engines{e}, evidence);  margX = marginal_nodes(engines{e}, X);  assert(approxeq(margX.mu, 1/2))  assert(approxeq(margX.Sigma, 2/3))    margY = marginal_nodes(engines{e}, Y);  assert(approxeq(margY.mu, 1))  assert(approxeq(margY.Sigma, 2/3))end