Mauch MIREX 2010

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