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1 function [engine, loglik] = enter_evidence(engine, evidence, varargin)
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2 % ENTER_EVIDENCE Add the specified evidence to the network (cond_gauss)
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3 % [engine, loglik] = enter_evidence(engine, evidence, ...)
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4 %
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5 % evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector)
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6
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7 bnet = bnet_from_engine(engine);
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8 ns = bnet.node_sizes(:);
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9 observed = ~isemptycell(evidence);
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10 onodes = find(observed);
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11 hnodes = find(isemptycell(evidence));
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12 engine.evidence = evidence;
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13
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14 % check there are no C->D links where C is hidden
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15 pot_type = determine_pot_type(bnet, onodes);
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16
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17 dhid = myintersect(hnodes, bnet.dnodes);
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18 S = prod(ns(dhid));
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19 T = zeros(S,1);
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20
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21 N = length(bnet.dag);
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22 mu = cell(1,N);
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23 Sigma = cell(1,N);
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24 cobs = myintersect(bnet.cnodes, onodes);
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25 chid = myintersect(bnet.cnodes, hnodes);
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26 ens = ns;
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27 ens(cobs) = 0;
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28 for j=chid(:)'
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29 mu{j} = zeros(ens(j), S);
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30 Sigma{j} = zeros(ens(j), ens(j), S);
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31 end
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32
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33 for i=1:S
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34 dvals = ind2subv(ns(dhid), i);
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35 evidence(dhid) = num2cell(dvals);
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36 [sub_engine, loglik] = enter_evidence(engine.sub_engine, evidence);
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37 for j=chid(:)'
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38 m = marginal_nodes(sub_engine, j);
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39 mu{j}(:,i) = m.mu;
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40 Sigma{j}(:,:,i) = m.Sigma;
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41 end
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42 T(i) = exp(loglik);
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43 end
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44
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45 [T, lik] = normalise(T);
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46 loglik = log(lik);
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47
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48 engine.T = T;
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49 engine.mu = mu;
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50 engine.Sigma = Sigma;
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51
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52 dnodes = bnet.dnodes;
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53 dobs = myintersect(dnodes, onodes);
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54 ens(dobs) = 1;
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55 engine.joint_dmarginal = dpot(dnodes, ens(dnodes), myreshape(engine.T, ens(dnodes)));
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56
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57 engine.onodes = onodes;
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