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1 function L = log_lik_complete(bnet, cases, clamped)
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2 % 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
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3 % L = log_lik_complete(bnet, cases, clamped)
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4 %
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5 % If there is a missing data, you must use an inference engine.
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6 % cases(i,m) is the value assigned to node i in case m.
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7 % (If there are vector-valued nodes, cases should be a cell array.)
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8 % clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros)
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9 % Clamped nodes contribute a factor of 1.0 to the likelihood.
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10
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11 if iscell(cases), usecell = 1; else usecell = 0; end
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12
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13 n = length(bnet.dag);
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14 ncases = size(cases, 2);
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15 if n ~= size(cases, 1)
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16 error('data should be of size nnodes * ncases');
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17 end
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18
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19 if nargin < 3, clamped = zeros(n,ncases); end
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20
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21 L = 0;
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22 for i=1:n
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23 ps = parents(bnet.dag, i);
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24 e = bnet.equiv_class(i);
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25 u = find(clamped(i,:)==0);
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26 ll = log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u));
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27 if approxeq(exp(ll), 0), fprintf('node %d has very low likelihood\n'); end
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28 L = L + ll;
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29 end
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30
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