wolffd@0: function CPD = learn_params(CPD, fam, data, ns, cnodes)
wolffd@0: %function CPD = learn_params(CPD, local_data)
wolffd@0: % LEARN_PARAMS Compute the ML/MAP estimate of the params of a tabular CPD given complete data
wolffd@0: % CPD = learn_params(CPD, local_data)
wolffd@0: %
wolffd@0: % local_data(i,m) is the value of i'th family member in case m (can be cell array).
wolffd@0: 
wolffd@0: local_data = data(fam, :); 
wolffd@0: if iscell(local_data)
wolffd@0:   local_data = cell2num(local_data);
wolffd@0: end
wolffd@0: counts = compute_counts(local_data, CPD.sizes);
wolffd@0: switch CPD.prior_type
wolffd@0:  case 'none', CPD.CPT = mk_stochastic(counts); 
wolffd@0:  case 'dirichlet', CPD.CPT = mk_stochastic(counts + CPD.dirichlet); 
wolffd@0:  otherwise, error(['unrecognized prior ' CPD.prior_type])
wolffd@0: end