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% 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)
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