Mauch MIREX 2010

function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)

% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node

% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)

%if nargin < 6

%  hidden_bitv = zeros(1, max(fmarginal.domain));

%  hidden_bitv(find(isempty(evidence)))=1;

%end

dom = fmarginal.domain;

self = dom(end);

ps = dom(1:end-1);

hidden_self = hidden_bitv(self);

cps = myintersect(ps, cnodes);

dps = mysetdiff(ps, cps);

hidden_cps = all(hidden_bitv(cps));

hidden_dps = all(hidden_bitv(dps));

CPD.nsamples = CPD.nsamples + 1;            

[ss cpsz dpsz] = size(CPD.weights); % ss = self size

% Let X be the cts parent (if any), Y be the cts child (self).

if ~hidden_self & (isempty(cps) | ~hidden_cps) & hidden_dps % all cts nodes are observed, all discrete nodes are hidden

  % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0

  % Since discrete parents are hidden, we do not need to add evidence to w.

  w = fmarginal.T(:);

  CPD.Wsum = CPD.Wsum + w;

  y = evidence{self};

  Cyy = y*y';

  if ~CPD.useC

     W = repmat(w(:)',ss,1); % W(y,i) = w(i)

     W2 = repmat(reshape(W, [ss 1 dpsz]), [1 ss 1]); % W2(x,y,i) = w(i)

     CPD.WYsum = CPD.WYsum +  W .* repmat(y(:), 1, dpsz);

     CPD.WYYsum = CPD.WYYsum + W2  .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]);

  else

     W = w(:)';

     W2 = reshape(W, [1 1 dpsz]);

     CPD.WYsum = CPD.WYsum +  rep_mult(W, y(:), size(CPD.WYsum)); 

     CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum));

  end

  if cpsz > 0 % X exists

    x = cat(1, evidence{cps}); x = x(:);

    Cxx = x*x';

    Cxy = x*y';

    if ~CPD.useC

       CPD.WXsum = CPD.WXsum + W .* repmat(x(:), 1, dpsz);

       CPD.WXXsum = CPD.WXXsum + W2 .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]);

       CPD.WXYsum = CPD.WXYsum + W2 .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]);

    else

       CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum));

       CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum));

       CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum));

    end

  end

  return;

end

% general (non-vectorized) case

fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow!

if dpsz == 1 % no discrete parents

  w = 1;

else

  w = fullm.T(:);

end

CPD.Wsum = CPD.Wsum + w;

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.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX');

    CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY');

  end

end