camir-aes2014: toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/gaussian_inf

first hg version after svn

rev	line source
wolffd@0	1 function engine = gaussian_inf_engine(bnet)
wolffd@0	2 % GAUSSIAN_INF_ENGINE Computes the joint multivariate Gaussian corresponding to the bnet
wolffd@0	3 % engine = gaussian_inf_engine(bnet)
wolffd@0	4 %
wolffd@0	5 % For details on how to compute the joint Gaussian from the bnet, see
wolffd@0	6 % - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989.
wolffd@0	7 % Once we have the Gaussian, we can apply the standard formulas for conditioning and marginalization.
wolffd@0	8
wolffd@0	9 assert(isequal(bnet.cnodes, 1:length(bnet.dag)));
wolffd@0	10
wolffd@0	11 [W, D, mu] = extract_params_from_gbn(bnet);
wolffd@0	12 U = inv(eye(size(W)) - W')';
wolffd@0	13 Sigma = U' * D * U;
wolffd@0	14
wolffd@0	15 engine.mu = mu;
wolffd@0	16 engine.Sigma = Sigma;
wolffd@0	17 %engine.logp = log(normal_coef(Sigma));
wolffd@0	18
wolffd@0	19 % This is where we will store the results between enter_evidence and marginal_nodes
wolffd@0	20 engine.Hmu = [];
wolffd@0	21 engine.HSigma = [];
wolffd@0	22 engine.hnodes = [];
wolffd@0	23
wolffd@0	24 engine = class(engine, 'gaussian_inf_engine', inf_engine(bnet));
wolffd@0	25

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