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1 function [prior, transmat] = dbn_to_hmm(bnet)
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2 % DBN_TO_HMM Compute the discrete HMM matrices from a simple DBN
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3 % [prior, transmat] = dbn_to_hmm(bnet)
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4
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5 onodes = bnet.observed;
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6 ss = length(bnet.intra);
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7 evidence = cell(1,2*ss);
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8 hnodes = mysetdiff(1:ss, onodes);
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9 prior = multiply_CPTs(bnet, [], hnodes, evidence);
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10 transmat = multiply_CPTs(bnet, hnodes, hnodes+ss, evidence);
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11 %obsmat1 = multiply_CPTs(bnet, hnodes, onodes, evidence);
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12 %obsmat = multiply_CPTs(bnet, hnodes+ss, onodes+ss, evidence);
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13 %obsmat1 = obsmat if the observation matrices are tied across slices
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14
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15
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16
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17 %%%%%%%%%%%%
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18
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19 function mat = multiply_CPTs(bnet, pdom, cdom, evidence)
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20
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21 % MULTIPLY_CPTS Make a matrix Pr(Y|X), where X represents all the parents, and Y all the children
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22 % We assume the children have no intra-connections.
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23 %
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24 % e.g., Consider the DBN with interconnectivity i->i', j->j',k', k->i',k'
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25 % Then transition matrix = Pr(i,j,k -> i',j',k') = Pr(i,k->i') Pr(j->j') Pr(j,k->k')
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26
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27 dom = [pdom cdom];
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28 ns = bnet.node_sizes;
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29 bigpot = dpot(dom, ns(dom));
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30 for j=cdom(:)'
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31 e = bnet.equiv_class(j);
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32 fam = family(bnet.dag, j);
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33 pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence);
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34 bigpot = multiply_by_pot(bigpot, pot);
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35 end
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36 psize = prod(ns(pdom));
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37 csize = prod(ns(cdom));
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38 T = pot_to_marginal(bigpot);
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39 mat = reshape(T.T, [psize csize]);
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40
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41
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