Daniel@0: function bnet = bayes_update_params(bnet, cases, clamped)
Daniel@0: % BAYES_UPDATE_PARAMS Bayesian parameter updating given completely observed data
Daniel@0: % bnet = bayes_update_params(bnet, cases, clamped)
Daniel@0: %
Daniel@0: % If there is a missing data, you must use EM.
Daniel@0: % cases(i,m) is the value assigned to node i in case m (this can also be a cell array).
Daniel@0: % clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros).
Daniel@0: % Clamped nodes are not updated.
Daniel@0: % If there is a single case, clamped is a list of the clamped nodes, not a bit vector.
Daniel@0: 
Daniel@0: 
Daniel@0: %if iscell(cases), usecell = 1; else usecell = 0; end
Daniel@0: 
Daniel@0: n = length(bnet.dag);
Daniel@0: ncases = size(cases, 2);
Daniel@0: if n ~= size(cases, 1)
Daniel@0:   error('data must be of size nnodes * ncases');
Daniel@0: end
Daniel@0: 
Daniel@0: if ncases == 1 % clamped is a list of nodes
Daniel@0:   if nargin < 3, clamped = []; end
Daniel@0:   clamp_set = clamped;
Daniel@0:   clamped = zeros(n,1);
Daniel@0:   clamped(clamp_set) = 1;
Daniel@0: else % each row of clamped is a bit vector
Daniel@0:   if nargin < 3, clamped = zeros(n,ncases); end
Daniel@0: end
Daniel@0: 
Daniel@0: for i=1:n
Daniel@0:   e = bnet.equiv_class(i);
Daniel@0:   if adjustable_CPD(bnet.CPD{e})
Daniel@0:     u = find(clamped(i,:)==0);
Daniel@0:     ps = parents(bnet.dag, i);
Daniel@0:     bnet.CPD{e} = bayes_update_params(bnet.CPD{e}, cases(i,u), cases(ps,u));
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: