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1 function CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence)
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2 % CONVERT_DBN_CPDS_TO_TABLES_SLOW Convert CPDs of (possibly instantiated) DBN nodes to tables
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3 % CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence)
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4 %
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5 % CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev)
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6 % All hidden nodes are assumed to be discrete
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7 %
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8 % Non-vectorized method; this is less efficient for long sequences of observed Gaussian
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9 % nodes, because of the (unnecessary) repeated matrix inversion.
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10
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11 obs_bitv = ~isemptycell(evidence(:));
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12 [ss T] = size(evidence);
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13 ns = bnet.node_sizes(:);
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14
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15 CPDpot = cell(ss,T);
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16
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17 t = 1;
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18 for n=1:ss
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19 %ps = engine.bnet_parents{n};
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20 ps = parents(bnet.dag, n);
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21 e = bnet.equiv_class(n, 1);
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22 if ~any(obs_bitv(ps))
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23 CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t});
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24 else
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25 CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1));
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26 end
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27 end
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28 for t=2:T
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29 for n=1:ss
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30 self = n+ss;
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31 ps = parents(bnet.dag, self);
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32 e = bnet.equiv_class(n, 2);
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33 if ~any(obs_bitv(ps))
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34 CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t});
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35 else
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36 CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t));
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37 end
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38 end
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39 end
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40
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41
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