Mercurial > hg > camir-aes2014
view 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 |
---|---|
date | Tue, 10 Feb 2015 15:05:51 +0000 |
parents | |
children |
line wrap: on
line source
function engine = gaussian_inf_engine(bnet) % GAUSSIAN_INF_ENGINE Computes the joint multivariate Gaussian corresponding to the bnet % engine = gaussian_inf_engine(bnet) % % For details on how to compute the joint Gaussian from the bnet, see % - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989. % Once we have the Gaussian, we can apply the standard formulas for conditioning and marginalization. assert(isequal(bnet.cnodes, 1:length(bnet.dag))); [W, D, mu] = extract_params_from_gbn(bnet); U = inv(eye(size(W)) - W')'; Sigma = U' * D * U; engine.mu = mu; engine.Sigma = Sigma; %engine.logp = log(normal_coef(Sigma)); % This is where we will store the results between enter_evidence and marginal_nodes engine.Hmu = []; engine.HSigma = []; engine.hnodes = []; engine = class(engine, 'gaussian_inf_engine', inf_engine(bnet));