% Conditional Gaussian network% The waste incinerator emissions example from Lauritzen (1992),% "Propogation of Probabilities, Means and Variances in Mixed Graphical Association Models", % JASA 87(420): 1098--1108%% This example is reprinted on p145 of "Probabilistic Networks and Expert Systems",% Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer.%% For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#cg_modelns = 2*ones(1,9);%bnet  = mk_incinerator_bnet(ns);bnet  = mk_incinerator_bnet;engines = {};%engines{end+1} = stab_cond_gauss_inf_engine(bnet);engines{end+1} = jtree_inf_engine(bnet);engines{end+1} = cond_gauss_inf_engine(bnet);nengines = length(engines);F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9;n = 9;dnodes = [B F W];cnodes = mysetdiff(1:n, dnodes);evidence = cell(1,n); % no evidencell = zeros(1, nengines);for e=1:nengines  [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence);end%assert(approxeq(ll(1), ll)))ll% Compare to the results in table on p1107.% These results are printed to 3dp in Cowell p150mu = zeros(1,n);sigma = zeros(1,n);dprob = zeros(1,n);addev = 1;tol = 1e-2;for e=1:nengines  for i=cnodes(:)'    m = marginal_nodes(engines{e}, i, addev);    mu(i) = m.mu;    sigma(i) = sqrt(m.Sigma);  end  for i=dnodes(:)'    m = marginal_nodes(engines{e}, i, addev);    dprob(i) = m.T(1);  end  assert(approxeq(mu([E D C L Min Mout]), [-3.25 3.04 -1.85 1.48 -0.214 2.83], tol))  assert(approxeq(sigma([E D C L Min Mout]), [0.709 0.770 0.507 0.631 0.459 0.860], tol))  assert(approxeq(dprob([B F W]), [0.85 0.95 0.29], tol))  %m = marginal_nodes(engines{e}, bnet.names('E'), addev);  %assert(approxeq(m.mu, -3.25, tol))  %assert(approxeq(sqrt(m.Sigma), 0.709, tol))end% Add evidence (p 1105, top right)evidence = cell(1,n);evidence{W} = 1; % industrialevidence{L} = 1.1;evidence{C} = -0.9;ll = zeros(1, nengines);for e=1:nengines  [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence);endassert(all(approxeq(ll(1), ll)))for e=1:nengines  for i=cnodes(:)'    m = marginal_nodes(engines{e}, i, addev);    mu(i) = m.mu;    sigma(i) = sqrt(m.Sigma);  end  for i=dnodes(:)'    m = marginal_nodes(engines{e}, i, addev);    dprob(i) = m.T(1);  end  assert(approxeq(mu([E D C L Min Mout]), [-3.90 3.61 -0.9 1.1 0.5 4.11], tol))  assert(approxeq(sigma([E D C L Min Mout]), [0.076 0.326 0 0 0.1 0.344], tol))  assert(approxeq(dprob([B F W]), [0.0122 0.9995 1], tol))end