Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/gaussian_inf_engine.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 engine = gaussian_inf_engine(bnet) | |
2 % GAUSSIAN_INF_ENGINE Computes the joint multivariate Gaussian corresponding to the bnet | |
3 % engine = gaussian_inf_engine(bnet) | |
4 % | |
5 % For details on how to compute the joint Gaussian from the bnet, see | |
6 % - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989. | |
7 % Once we have the Gaussian, we can apply the standard formulas for conditioning and marginalization. | |
8 | |
9 assert(isequal(bnet.cnodes, 1:length(bnet.dag))); | |
10 | |
11 [W, D, mu] = extract_params_from_gbn(bnet); | |
12 U = inv(eye(size(W)) - W')'; | |
13 Sigma = U' * D * U; | |
14 | |
15 engine.mu = mu; | |
16 engine.Sigma = Sigma; | |
17 %engine.logp = log(normal_coef(Sigma)); | |
18 | |
19 % This is where we will store the results between enter_evidence and marginal_nodes | |
20 engine.Hmu = []; | |
21 engine.HSigma = []; | |
22 engine.hnodes = []; | |
23 | |
24 engine = class(engine, 'gaussian_inf_engine', inf_engine(bnet)); | |
25 |