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1 function L = log_prob_node(CPD, self_ev, pev)
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2 % LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian)
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3 % L = log_prob_node(CPD, self_ev, pev)
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4 %
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5 % self_ev(m) is the evidence on this node in case m.
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6 % pev(i,m) is the evidence on the i'th parent in case m (if there are any parents).
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7 % (These may also be cell arrays.)
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8
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9 if iscell(self_ev), usecell = 1; else usecell = 0; end
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10
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11 use_log = 1;
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12 ncases = length(self_ev);
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13 nparents = length(CPD.sizes)-1;
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14 assert(ncases == size(pev, 2));
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15
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16 if ncases == 0
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17 L = 0;
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18 return;
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19 end
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20
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21 if length(CPD.dps)==0 % no discrete parents, so we can vectorize
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22 i = 1;
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23 if usecell
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24 Y = cell2num(self_ev);
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25 else
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26 Y = self_ev;
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27 end
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28 if length(CPD.cps) == 0
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29 L = gaussian_prob(Y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);
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30 else
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31 if usecell
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32 X = cell2num(pev);
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33 else
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34 X = pev;
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35 end
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36 L = gaussian_prob(Y, CPD.mean(:,i) + CPD.weights(:,:,i)*X, CPD.cov(:,:,i), use_log);
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37 end
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38 else % each case uses a (potentially) different set of parameters
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39 L = 0;
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40 for m=1:ncases
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41 if usecell
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42 dpvals = cat(1, pev{CPD.dps, m});
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43 else
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44 dpvals = pev(CPD.dps, m);
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45 end
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46 i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)');
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47 y = self_ev{m};
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48 if length(CPD.cps) == 0
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49 L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);
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50 else
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51 if usecell
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52 x = cat(1, pev{CPD.cps, m});
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53 else
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54 x = pev(CPD.cps, m);
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55 end
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56 L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log);
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57 end
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58 end
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59 end
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