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function bnet = mk_fhmm(N, Q, Y, discrete_obs)
% MK_FHMM Make a factorial Hidden Markov Model
%
% There are N independent parallel hidden chains, each connected to the output
%
% e.g., N = 2 (vertical/diagonal edges point down)
%
% A1--->A2
% | B1--|->B2
% | / |/
% Y1 Y2
%
% [bnet, onode] = mk_chmm(n, q, y, discrete_obs)
%
% Each hidden node is discrete and has Q values.
% If discrete_obs = 1, each observed node is discrete and has values 1..Y.
% If discrete_obs = 0, each observed node is a Gaussian vector of length Y.
if nargin < 2, Q = 2; end
if nargin < 3, Y = 2; end
if nargin < 4, discrete_obs = 1; end
ss = N+1;
hnodes = 1:N;
onode = N+1;
intra = zeros(ss);
intra(hnodes, onode) = 1;
inter = eye(ss);
inter(onode,onode) = 0;
ns = [Q*ones(1,N) Y];
eclass1 = [hnodes onode];
eclass2 = [hnodes+ss onode];
if discrete_obs
dnodes = 1:ss;
else
dnodes = hnodes;
end
bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
'observed', onode);
for i=hnodes(:)'
bnet.CPD{i} = tabular_CPD(bnet, i);
end
i = onode;
if discrete_obs
bnet.CPD{i} = tabular_CPD(bnet, i);
else
bnet.CPD{i} = gaussian_CPD(bnet, i);
end
for i=hnodes(:)'+ss
bnet.CPD{i} = tabular_CPD(bnet, i);
end
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