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;enduse_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);