Daniel@0: function CPD = learn_params(CPD, fam, data, ns, cnodes)
Daniel@0: % LEARN_PARAMS Compute the maximum likelihood estimate of the params of a generic CPD given complete data
Daniel@0: % CPD = learn_params(CPD, fam, data, ns, cnodes)
Daniel@0: %
Daniel@0: % data(i,m) is the value of node i in case m (can be cell array).
Daniel@0: % We assume this node has a maximize_params method.
Daniel@0: 
Daniel@0: %error('no longer supported') % KPM 1 Feb 03
Daniel@0: 
Daniel@0: if 1
Daniel@0: ncases = size(data, 2);
Daniel@0: CPD = reset_ess(CPD);
Daniel@0: % make a fully observed joint distribution over the family
Daniel@0: fmarginal.domain = fam;
Daniel@0: fmarginal.T = 1;
Daniel@0: fmarginal.mu = [];
Daniel@0: fmarginal.Sigma = [];
Daniel@0: if ~iscell(data)
Daniel@0:   cases = num2cell(data);
Daniel@0: else
Daniel@0:   cases = data;
Daniel@0: end
Daniel@0: hidden_bitv = zeros(1, max(fam));
Daniel@0: for m=1:ncases
Daniel@0:   % specify (as a bit vector) which elements in the family domain are hidden
Daniel@0:   hidden_bitv = zeros(1, max(fmarginal.domain));
Daniel@0:   ev = cases(:,m);
Daniel@0:   hidden_bitv(find(isempty(evidence)))=1;
Daniel@0:   CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv);
Daniel@0: end
Daniel@0: CPD = maximize_params(CPD);
Daniel@0: end