wolffd@0: function CPD = update_tied_ess(CPD, domain, engine, evidence, ns, cnodes)
wolffd@0: 
wolffd@0: if ~adjustable_CPD(CPD), return; end
wolffd@0: nCPDs = size(domain, 2);
wolffd@0: fmarginal = cell(1, nCPDs);
wolffd@0: for l=1:nCPDs
wolffd@0:   fmarginal{l} = marginal_family(engine, nodes(l));
wolffd@0: end
wolffd@0: 
wolffd@0: [ss cpsz dpsz] = size(CPD.weights);
wolffd@0: if const_evidence_pattern(engine)
wolffd@0:   dom = domain(:,1);
wolffd@0:   dnodes = mysetdiff(1:length(ns), cnodes);
wolffd@0:   ddom = myintersect(dom, dnodes);
wolffd@0:   cdom = myintersect(dom, cnodes);
wolffd@0:   odom = dom(~isemptycell(evidence(dom)));
wolffd@0:   hdom = dom(isemptycell(evidence(dom)));
wolffd@0:   % If all hidden nodes are discrete and all cts nodes are observed 
wolffd@0:   % (e.g., HMM with Gaussian output)
wolffd@0:   % we can add the observed evidence in parallel
wolffd@0:   if mysubset(ddom, hdom) & mysubset(cdom, odom)
wolffd@0:     [mu, Sigma, T] = add_cts_ev_to_marginals(fmarginal, evidence, ns, cnodes);
wolffd@0:   else
wolffd@0:     mu = zeros(ss, dpsz, nCPDs);
wolffd@0:     Sigma = zeros(ss, ss, dpsz, nCPDs);
wolffd@0:     T = zeros(dpsz, nCPDs);
wolffd@0:     for l=1:nCPDs
wolffd@0:       [mu(:,:,l), Sigma(:,:,:,l), T(:,l)] = add_ev_to_marginals(fmarginal{l}, evidence, ns, cnodes);
wolffd@0:     end
wolffd@0:   end
wolffd@0: end
wolffd@0: CPD.nsamples = CPD.nsamples + nCPDs;            
wolffd@0: 
wolffd@0: 
wolffd@0: if dpsz == 1 % no discrete parents
wolffd@0:   w = 1;
wolffd@0: else
wolffd@0:   w = fullm.T(:);
wolffd@0: end
wolffd@0: CPD.Wsum = CPD.Wsum + w;
wolffd@0: % Let X be the cts parent (if any), Y be the cts child (self).
wolffd@0: xi = 1:cpsz;
wolffd@0: yi = (cpsz+1):(cpsz+ss);
wolffd@0: for i=1:dpsz
wolffd@0:   muY = fullm.mu(yi, i);
wolffd@0:   SYY = fullm.Sigma(yi, yi, i);
wolffd@0:   CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY;
wolffd@0:   CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y]
wolffd@0:   if cpsz > 0
wolffd@0:     muX = fullm.mu(xi, i);
wolffd@0:     SXX = fullm.Sigma(xi, xi, i);
wolffd@0:     SXY = fullm.Sigma(xi, yi, i);
wolffd@0:     CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX;
wolffd@0:     CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY');
wolffd@0:     CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX');
wolffd@0:   end
wolffd@0: end                
wolffd@0: 
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%
wolffd@0: 
wolffd@0: function fullm = add_evidence_to_marginal(fmarginal, evidence, ns, cnodes)
wolffd@0: 
wolffd@0: 
wolffd@0: dom = fmarginal.domain;
wolffd@0: 
wolffd@0: % Find out which values of the discrete parents (if any) are compatible with 
wolffd@0: % the discrete evidence (if any).
wolffd@0: dnodes = mysetdiff(1:length(ns), cnodes);
wolffd@0: ddom = myintersect(dom, dnodes);
wolffd@0: cdom = myintersect(dom, cnodes);
wolffd@0: odom = dom(~isemptycell(evidence(dom)));
wolffd@0: hdom = dom(isemptycell(evidence(dom)));
wolffd@0: 
wolffd@0: dobs = myintersect(ddom, odom);
wolffd@0: dvals = cat(1, evidence{dobs});
wolffd@0: ens = ns; % effective node sizes
wolffd@0: ens(dobs) = 1;
wolffd@0: S = prod(ens(ddom));
wolffd@0: subs = ind2subv(ens(ddom), 1:S);
wolffd@0: mask = find_equiv_posns(dobs, ddom);
wolffd@0: subs(mask) = dvals;
wolffd@0: supportedQs = subv2ind(ns(ddom), subs);
wolffd@0: 
wolffd@0: if isempty(ddom)
wolffd@0:   Qarity = 1;
wolffd@0: else
wolffd@0:   Qarity = prod(ns(ddom));
wolffd@0: end
wolffd@0: fullm.T = zeros(Qarity, 1);
wolffd@0: fullm.T(supportedQs) = fmarginal.T(:);
wolffd@0: 
wolffd@0: % Now put the hidden cts parts into their right blocks,
wolffd@0: % leaving the observed cts parts as 0.
wolffd@0: cobs = myintersect(cdom, odom);
wolffd@0: chid = myintersect(cdom, hdom);
wolffd@0: cvals = cat(1, evidence{cobs});
wolffd@0: n = sum(ns(cdom));
wolffd@0: fullm.mu = zeros(n,Qarity);
wolffd@0: fullm.Sigma = zeros(n,n,Qarity);
wolffd@0: 
wolffd@0: if ~isempty(chid)
wolffd@0:   chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom));
wolffd@0: end
wolffd@0: if ~isempty(cobs)
wolffd@0:   cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom));
wolffd@0: end
wolffd@0: 
wolffd@0: for i=1:length(supportedQs)
wolffd@0:   Q = supportedQs(i);
wolffd@0:   if ~isempty(chid)
wolffd@0:     fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i);
wolffd@0:     fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i);
wolffd@0:   end
wolffd@0:   if ~isempty(cobs)
wolffd@0:     fullm.mu(cobs_blocks, Q) = cvals(:);
wolffd@0:   end
wolffd@0: end