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function L = log_marg_lik_complete(bnet, cases, clamped)
% LOG_MARG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m)) for a completely observed data set
% L = log_marg_lik_complete(bnet, cases, clamped)
%
% This differs from log_lik_complete because we integrate out the parameters.
% If there is a missing data, you must use an inference engine.
% cases(i,m) is the value assigned to node i in case m.
% (If there are vector-valued nodes, cases should be a cell array.)
% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros)
% Clamped nodes contribute a factor of 1.0 to the likelihood.
%
% 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 should be of size nnodes * ncases');
end
if ncases == 1
if nargin < 3, clamped = []; end
clamp_set = clamped;
clamped = zeros(n,1);
clamped(clamp_set) = 1;
else
if nargin < 3, clamped = zeros(n,ncases); end
end
L = 0;
for i=1:n
ps = parents(bnet.dag, i);
e = bnet.equiv_class(i);
u = find(clamped(i,:)==0);
L = L + log_marg_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u));
end
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