Daniel@0: function CPD = bayes_update_params(CPD, self_ev, pev)
Daniel@0: % UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular)
Daniel@0: % CPD = update_params_complete(CPD, self_ev, pev)
Daniel@0: %
Daniel@0: % self_ev(m) is the evidence on this node in case m.
Daniel@0: % pev(i,m) is the evidence on the i'th parent in case m (if there are any parents).
Daniel@0: % These can be arrays or cell arrays.
Daniel@0: %
Daniel@0: % We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior.
Daniel@0: 
Daniel@0: if iscell(self_ev), usecell = 1; else usecell = 0; end
Daniel@0: 
Daniel@0: ncases = length(self_ev);
Daniel@0: sz = CPD.sizes;
Daniel@0: nparents = length(sz)-1;
Daniel@0: assert(nparents == size(pev,1));
Daniel@0: 
Daniel@0: if ncases == 0 | ~adjustable_CPD(CPD)
Daniel@0:   return;
Daniel@0: elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array
Daniel@0:   if usecell
Daniel@0:     x = cat(1, pev{:})';
Daniel@0:     y = self_ev{1};
Daniel@0:   else
Daniel@0:     x = pev(:)';
Daniel@0:     y = self_ev;
Daniel@0:   end
Daniel@0:   switch nparents
Daniel@0:    case 0,
Daniel@0:     CPD.dirichlet(y) = CPD.dirichlet(y)+1;
Daniel@0:     CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet);
Daniel@0:    case 1,
Daniel@0:     CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1;
Daniel@0:     CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :));
Daniel@0:    case 2,
Daniel@0:     CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1;
Daniel@0:     CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :));
Daniel@0:    case 3,
Daniel@0:     CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1;
Daniel@0:     CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :));
Daniel@0:    otherwise,
Daniel@0:     ind = subv2ind(sz, [x y]);
Daniel@0:     CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1;
Daniel@0:     CPD.CPT = mk_stochastic(CPD.dirichlet);
Daniel@0:   end
Daniel@0: else  
Daniel@0:   if usecell
Daniel@0:     data = [cell2num(pev); cell2num(self_ev)]; 
Daniel@0:   else
Daniel@0:     data = [pev; self_ev];
Daniel@0:   end
Daniel@0:   counts = compute_counts(data, sz);
Daniel@0:   CPD.dirichlet = CPD.dirichlet + counts;
Daniel@0:   CPD.CPT = mk_stochastic(CPD.dirichlet);
Daniel@0: end