function engine = gaussian_inf_engine(bnet)% GAUSSIAN_INF_ENGINE Computes the joint multivariate Gaussian corresponding to the bnet% engine = gaussian_inf_engine(bnet)%% For details on how to compute the joint Gaussian from the bnet, see% - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989.% Once we have the Gaussian, we can apply the standard formulas for conditioning and marginalization.assert(isequal(bnet.cnodes, 1:length(bnet.dag)));[W, D, mu] = extract_params_from_gbn(bnet);U = inv(eye(size(W)) - W')';Sigma = U' * D * U;engine.mu = mu;engine.Sigma = Sigma;%engine.logp = log(normal_coef(Sigma));% This is where we will store the results between enter_evidence and marginal_nodes  engine.Hmu = [];engine.HSigma = [];engine.hnodes = [];engine = class(engine, 'gaussian_inf_engine', inf_engine(bnet));