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1 function bnet = bayes_update_params(bnet, cases, clamped)
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2 % BAYES_UPDATE_PARAMS Bayesian parameter updating given completely observed data
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3 % bnet = bayes_update_params(bnet, cases, clamped)
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4 %
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5 % If there is a missing data, you must use EM.
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6 % cases(i,m) is the value assigned to node i in case m (this can also be a cell array).
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7 % clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros).
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8 % Clamped nodes are not updated.
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9 % If there is a single case, clamped is a list of the clamped nodes, not a bit vector.
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10
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11
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12 %if iscell(cases), usecell = 1; else usecell = 0; end
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13
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14 n = length(bnet.dag);
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15 ncases = size(cases, 2);
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16 if n ~= size(cases, 1)
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17 error('data must be of size nnodes * ncases');
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18 end
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19
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20 if ncases == 1 % clamped is a list of nodes
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21 if nargin < 3, clamped = []; end
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22 clamp_set = clamped;
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23 clamped = zeros(n,1);
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24 clamped(clamp_set) = 1;
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25 else % each row of clamped is a bit vector
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26 if nargin < 3, clamped = zeros(n,ncases); end
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27 end
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28
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29 for i=1:n
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30 e = bnet.equiv_class(i);
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31 if adjustable_CPD(bnet.CPD{e})
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32 u = find(clamped(i,:)==0);
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33 ps = parents(bnet.dag, i);
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34 bnet.CPD{e} = bayes_update_params(bnet.CPD{e}, cases(i,u), cases(ps,u));
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35 end
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36 end
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37
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38
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