Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CPD_to_lambda_msg.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) | |
2 % CPD_TO_LAMBDA_MSG Compute lambda message (gmux) | |
3 % lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) | |
4 % Pearl p183 eq 4.52 | |
5 | |
6 % Let Y be this node, X1..Xn be the cts parents and M the discrete switch node. | |
7 % e.g., for n=3, M=1 | |
8 % | |
9 % X1 X2 X3 M | |
10 % \ | |
11 % \ | |
12 % Y | |
13 % | |
14 % So the only case in which we send an informative message is if p=1=M. | |
15 % To the other cts parents, we send the "know nothing" message. | |
16 | |
17 switch msg_type | |
18 case 'd', | |
19 error('gaussian_CPD can''t create discrete msgs') | |
20 case 'g', | |
21 cps = ps(CPD.cps); | |
22 cpsizes = CPD.sizes(CPD.cps); | |
23 self_size = CPD.sizes(end); | |
24 i = find_equiv_posns(p, cps); % p is n's i'th cts parent | |
25 psz = cpsizes(i); | |
26 dps = ps(CPD.dps); | |
27 M = evidence{dps}; | |
28 if isempty(M) | |
29 error('gmux node must have observed discrete parent') | |
30 end | |
31 P = msg{n}.lambda.precision; | |
32 if all(P == 0) | (cps(M) ~= p) % if we know nothing, or are sending to a disconnected parent | |
33 lam_msg.precision = zeros(psz, psz); | |
34 lam_msg.info_state = zeros(psz, 1); | |
35 return; | |
36 end | |
37 % We are sending a message to the only effectively connected parent. | |
38 % There are no other incoming pi messages. | |
39 Bmu = CPD.mean(:,M); | |
40 BSigma = CPD.cov(:,:,M); | |
41 Bi = CPD.weights(:,:,M); | |
42 if (det(P) > 0) | isinf(P) | |
43 if isinf(P) % Y is observed | |
44 Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance | |
45 mu_lambda = msg{n}.lambda.mu; % observed_value; | |
46 else | |
47 Sigma_lambda = inv(P); | |
48 mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; | |
49 end | |
50 C = inv(Sigma_lambda + BSigma); | |
51 lam_msg.precision = Bi' * C * Bi; | |
52 lam_msg.info_state = Bi' * C * (mu_lambda - Bmu); | |
53 else | |
54 % method that uses matrix inversion lemma | |
55 A = inv(P + inv(BSigma)); | |
56 C = P - P*A*P; | |
57 lam_msg.precision = Bi' * C * Bi; | |
58 D = eye(self_size) - P*A; | |
59 z = msg{n}.lambda.info_state; | |
60 lam_msg.info_state = Bi' * (D*z - D*P*Bmu); | |
61 end | |
62 end |