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