wolffd@0: function pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence)
wolffd@0: % LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD  to a canonical potential.
wolffd@0: % pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence)
wolffd@0: %
wolffd@0: % We include any cts evidence, but ignore any discrete evidence.
wolffd@0: % (Use gaussian_CPD_params_given_dps to use discrete evidence to select mu, Sigma, W.)
wolffd@0: 
wolffd@0: odom = domain(~isemptycell(evidence(domain)));
wolffd@0: hdom = domain(isemptycell(evidence(domain)));
wolffd@0: cobs = myintersect(cnodes, odom);
wolffd@0: chid = myintersect(cnodes, hdom);
wolffd@0: cvals = cat(1, evidence{cobs});
wolffd@0: 
wolffd@0: %[g,h,K] = gaussian_to_canonical(mu, Sigma, W);
wolffd@0: Sinv = inv(Sigma);
wolffd@0: g = -0.5*mu'*Sinv*mu + log(normal_coef(Sigma));
wolffd@0: if isempty(W) | (size(W,2)==0)  % no cts parents
wolffd@0:   h = Sinv*mu;
wolffd@0:   K = Sinv;
wolffd@0: else
wolffd@0:   h = [-W'*Sinv*mu; Sinv*mu];
wolffd@0:   K = [W'*Sinv*W  -W'*Sinv';
wolffd@0:        -Sinv*W     Sinv]; 
wolffd@0: end
wolffd@0: 
wolffd@0: if ~isempty(cvals)
wolffd@0:   %[g, h, K] = enter_evidence_canonical(g, h, K, chid, cobs, cvals(:), ns);  
wolffd@0:   [hx, hy, KXX, KXY, KYX, KYY] = partition_matrix_vec(h, K, chid, cobs, ns);
wolffd@0:   y = cvals(:);
wolffd@0:   g = g + hy'*y - 0.5*y'*KYY*y;
wolffd@0:   if length(hx)==0 % isempty(X) % i.e., we have instantiated everything away
wolffd@0:     h = [];
wolffd@0:     K = [];
wolffd@0:   else
wolffd@0:     h = hx - KXY*y;
wolffd@0:     K = KXX;
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: ns(odom) = 0;
wolffd@0: pot = cpot(domain, ns(domain), g, h, K);
wolffd@0: 
wolffd@0: