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

first hg version after svn

+function [engine, loglik] = enter_evidence(engine, evidence, varargin)
+% ENTER_EVIDENCE Add the specified evidence to the network (gaussian_inf_engine)
+% [engine, loglik] = enter_evidence(engine, evidence, ...)
+%
+% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector)
+
+bnet = bnet_from_engine(engine);
+ns = bnet.node_sizes;
+O = find(~isemptycell(evidence));
+H = find(isemptycell(evidence));
+vals = cat(1, evidence{O});
+
+% Compute Pr(H|o)
+[Hmu, HSigma, loglik] = condition_gaussian(engine.mu, engine.Sigma, H, O, vals(:), ns);
+
+engine.Hmu = Hmu;
+engine.HSigma = HSigma;
+engine.hnodes = H;
+
+%%%%%%%%
+
+function [mu2, Sigma2, loglik] = condition_gaussian(mu, Sigma, X, Y, y, ns)
+% CONDITION_GAUSSIAN Compute Pr(X|Y=y) where X and Y are jointly Gaussian.
+% [mu2, Sigma2, ll] = condition_gaussian(mu, Sigma, X, Y, y, ns)
+
+if isempty(y)
+  mu2 = mu;
+  Sigma2 = Sigma;
+  loglik = 0;
+  return;
+end
+
+use_log = 1;
+
+if length(Y)==length(mu) % instantiating every variable
+  mu2 = y;
+  Sigma2 = zeros(length(y));
+  loglik = gaussian_prob(y, mu, Sigma, use_log);
+  return;
+end
+
+[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns);
+K = SXY*inv(SYY);
+mu2 = muX + K*(y-muY);
+Sigma2 = SXX - K*SYX;
+loglik = gaussian_prob(y, muY, SYY, use_log);