wolffd@0: function bnet = learn_params(bnet, data)
wolffd@0: % LEARN_PARAMS Find the maximum likelihood params for a fully observed model
wolffd@0: % bnet = learn_params(bnet, data)
wolffd@0: %
wolffd@0: % data(i,m) is the value of node i in case m (can be a cell array)
wolffd@0: %
wolffd@0: % We set bnet.CPD{i} to its ML/MAP estimate.
wolffd@0: %
wolffd@0: % Currently we assume no param tying
wolffd@0: 
wolffd@0: % AND THAT EACH DATA POINT IS A SCALAR - no longer assumed
wolffd@0: 
wolffd@0: %if iscell(data)
wolffd@0: %  data=cell2num(data);
wolffd@0: %end
wolffd@0: [n ncases] = size(data);
wolffd@0: for j=1:n
wolffd@0:   e = bnet.equiv_class(j);
wolffd@0:   assert(e==j);
wolffd@0:   if adjustable_CPD(bnet.CPD{e})
wolffd@0:     fam = family(bnet.dag,j);
wolffd@0:     %bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,:));
wolffd@0:     bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, bnet.node_sizes, bnet.cnodes);
wolffd@0:   end
wolffd@0: end
wolffd@0: