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wolffd@0
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1 function [engine, loglik] = enter_evidence(engine, evidence, varargin)
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wolffd@0
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2 % ENTER_EVIDENCE Add the specified evidence to the network (gaussian_inf_engine)
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wolffd@0
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3 % [engine, loglik] = enter_evidence(engine, evidence, ...)
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wolffd@0
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4 %
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wolffd@0
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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 O = find(~isemptycell(evidence));
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wolffd@0
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10 H = find(isemptycell(evidence));
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wolffd@0
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11 vals = cat(1, evidence{O});
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12
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13 % Compute Pr(H|o)
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14 [Hmu, HSigma, loglik] = condition_gaussian(engine.mu, engine.Sigma, H, O, vals(:), ns);
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15
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16 engine.Hmu = Hmu;
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17 engine.HSigma = HSigma;
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18 engine.hnodes = H;
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19
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20 %%%%%%%%
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21
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22 function [mu2, Sigma2, loglik] = condition_gaussian(mu, Sigma, X, Y, y, ns)
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wolffd@0
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23 % CONDITION_GAUSSIAN Compute Pr(X|Y=y) where X and Y are jointly Gaussian.
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wolffd@0
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24 % [mu2, Sigma2, ll] = condition_gaussian(mu, Sigma, X, Y, y, ns)
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wolffd@0
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25
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wolffd@0
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26 if isempty(y)
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27 mu2 = mu;
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wolffd@0
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28 Sigma2 = Sigma;
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29 loglik = 0;
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wolffd@0
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30 return;
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31 end
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32
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33 use_log = 1;
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34
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35 if length(Y)==length(mu) % instantiating every variable
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36 mu2 = y;
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wolffd@0
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37 Sigma2 = zeros(length(y));
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wolffd@0
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38 loglik = gaussian_prob(y, mu, Sigma, use_log);
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wolffd@0
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39 return;
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wolffd@0
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40 end
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wolffd@0
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41
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wolffd@0
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42 [muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns);
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wolffd@0
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43 K = SXY*inv(SYY);
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wolffd@0
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44 mu2 = muX + K*(y-muY);
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wolffd@0
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45 Sigma2 = SXX - K*SYX;
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wolffd@0
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46 loglik = gaussian_prob(y, muY, SYY, use_log);
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