diff toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,25 @@
+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));
+