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