Mauch MIREX 2010

function L = log_lik_complete(bnet, cases, clamped)

% LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set

% L = log_lik_complete(bnet, cases, clamped)

%

% 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 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 nargin < 3, clamped = zeros(n,ncases); end

L = 0;

for i=1:n

  ps = parents(bnet.dag, i);

  e = bnet.equiv_class(i);

  u = find(clamped(i,:)==0);

  ll = log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u));

  if approxeq(exp(ll), 0),  fprintf('node %d has very low likelihood\n'); end

  L = L + ll;

end