wolffd@0
|
1 function [engine, loglik] = enter_evidence(engine, evidence, varargin)
|
wolffd@0
|
2 % ENTER_EVIDENCE Add the specified evidence to the network (gaussian_inf_engine)
|
wolffd@0
|
3 % [engine, loglik] = enter_evidence(engine, evidence, ...)
|
wolffd@0
|
4 %
|
wolffd@0
|
5 % evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector)
|
wolffd@0
|
6
|
wolffd@0
|
7 bnet = bnet_from_engine(engine);
|
wolffd@0
|
8 ns = bnet.node_sizes;
|
wolffd@0
|
9 O = find(~isemptycell(evidence));
|
wolffd@0
|
10 H = find(isemptycell(evidence));
|
wolffd@0
|
11 vals = cat(1, evidence{O});
|
wolffd@0
|
12
|
wolffd@0
|
13 % Compute Pr(H|o)
|
wolffd@0
|
14 [Hmu, HSigma, loglik] = condition_gaussian(engine.mu, engine.Sigma, H, O, vals(:), ns);
|
wolffd@0
|
15
|
wolffd@0
|
16 engine.Hmu = Hmu;
|
wolffd@0
|
17 engine.HSigma = HSigma;
|
wolffd@0
|
18 engine.hnodes = H;
|
wolffd@0
|
19
|
wolffd@0
|
20 %%%%%%%%
|
wolffd@0
|
21
|
wolffd@0
|
22 function [mu2, Sigma2, loglik] = condition_gaussian(mu, Sigma, X, Y, y, ns)
|
wolffd@0
|
23 % CONDITION_GAUSSIAN Compute Pr(X|Y=y) where X and Y are jointly Gaussian.
|
wolffd@0
|
24 % [mu2, Sigma2, ll] = condition_gaussian(mu, Sigma, X, Y, y, ns)
|
wolffd@0
|
25
|
wolffd@0
|
26 if isempty(y)
|
wolffd@0
|
27 mu2 = mu;
|
wolffd@0
|
28 Sigma2 = Sigma;
|
wolffd@0
|
29 loglik = 0;
|
wolffd@0
|
30 return;
|
wolffd@0
|
31 end
|
wolffd@0
|
32
|
wolffd@0
|
33 use_log = 1;
|
wolffd@0
|
34
|
wolffd@0
|
35 if length(Y)==length(mu) % instantiating every variable
|
wolffd@0
|
36 mu2 = y;
|
wolffd@0
|
37 Sigma2 = zeros(length(y));
|
wolffd@0
|
38 loglik = gaussian_prob(y, mu, Sigma, use_log);
|
wolffd@0
|
39 return;
|
wolffd@0
|
40 end
|
wolffd@0
|
41
|
wolffd@0
|
42 [muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns);
|
wolffd@0
|
43 K = SXY*inv(SYY);
|
wolffd@0
|
44 mu2 = muX + K*(y-muY);
|
wolffd@0
|
45 Sigma2 = SXX - K*SYX;
|
wolffd@0
|
46 loglik = gaussian_prob(y, muY, SYY, use_log);
|