% Check sampling on a mixture of experts model%%  X \%  | |%  Q |%  | /%  Y%% where all arcs point down.% We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian.% Q is hidden, X and Y are observed.X = 1;Q = 2;Y = 3;dag = zeros(3,3);dag(X,[Q Y]) = 1;dag(Q,Y) = 1;ns = [1 2 2];dnodes = [2];bnet = mk_bnet(dag, ns, dnodes);x = 0.5;bnet.CPD{1} = root_CPD(bnet, 1, x);bnet.CPD{2} = softmax_CPD(bnet, 2);bnet.CPD{3} = gaussian_CPD(bnet, 3);data_case = sample_bnet(bnet, 'evidence', {0.8, [], []})ll = log_lik_complete(bnet, data_case)data_case = sample_bnet(bnet, 'evidence', {-11, [], []})ll = log_lik_complete(bnet, data_case)