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function [bnet, onodes] = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
% MK_HMM_BNET Make a (static( bnet to represent a hidden Markov model
% [bnet, onodes] = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
%
% T = num time slices
% Q = num hidden states
% O = size of the observed node (num discrete values or length of vector)
% cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete
% param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node
N = 2*T;
dag = zeros(N);
for i=1:T-1
dag(i,i+1)=1;
end
onodes = T+1:N;
for i=1:T
dag(i, onodes(i)) = 1;
end
if cts_obs
dnodes = 1:T;
else
dnodes = 1:N;
end
ns = [Q*ones(1,T) O*ones(1,T)];
if param_tying
eclass = [1 2*ones(1,T-1) 3*ones(1,T)];
else
eclass = 1:N;
end
bnet = mk_bnet(dag, ns, dnodes, eclass);
hnodes = mysetdiff(1:N, onodes);
if ~param_tying
for i=hnodes(:)'
bnet.CPD{i} = tabular_CPD(bnet, i);
end
if cts_obs
for i=onodes(:)'
bnet.CPD{i} = gaussian_CPD(bnet, i);
end
else
for i=onodes(:)'
bnet.CPD{i} = tabular_CPD(bnet, i);
end
end
else
bnet.CPD{1} = tabular_CPD(bnet, 1);
bnet.CPD{2} = tabular_CPD(bnet, 2);
if cts_obs
bnet.CPD{3} = gaussian_CPD(bnet, 3);
else
bnet.CPD{3} = tabular_CPD(bnet, 3);
end
end
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