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1 % This example is from Page.143 of "Probabilistic Networks and Expert Systems",
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2 % Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer.
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3
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4 X = 1; Y = 2; Z = 3;
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5 n = 3;
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6
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7 dag = zeros(n);
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8 dag(X, Y)=1;
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9 dag(Y, Z)=1;
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10
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11 ns = ones(1, n);
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12 dnodes = [];
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13
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14 bnet = mk_bnet(dag, ns, dnodes);
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15 bnet.CPD{X} = gaussian_CPD(bnet, X, 'mean', 0, 'cov', 1);
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16 bnet.CPD{Y} = gaussian_CPD(bnet, Y, 'mean', 0, 'cov', 1, 'weights', 1);
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17 bnet.CPD{Z} = gaussian_CPD(bnet, Z, 'mean', 0, 'cov', 1, 'weights', 1);
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18
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19 engines = {};
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20 engines{end+1} = jtree_inf_engine(bnet);
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21 engines{end+1} = stab_cond_gauss_inf_engine(bnet);
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22 nengines = length(engines);
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23
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24 evidence = cell(1,n);
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25 evidence{Y} = 1.5;
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26
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27 for e=1:nengines
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28 engines{e} = enter_evidence(engines{e}, evidence);
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29 margX = marginal_nodes(engines{e}, X);
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30 assert(approxeq(margX.mu, 0.75))
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31 assert(approxeq(margX.Sigma, 0.5))
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32
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33 margZ = marginal_nodes(engines{e}, Z);
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34 assert(approxeq(margZ.mu, 1.5))
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35 assert(approxeq(margZ.Sigma, 1))
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36 end
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37
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38
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39 evidence = cell(1,n);
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40 evidence{Z} = 1.5;
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41
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42 for e=1:nengines
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43 engines{e} = enter_evidence(engines{e}, evidence);
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44 margX = marginal_nodes(engines{e}, X);
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45 assert(approxeq(margX.mu, 1/2))
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46 assert(approxeq(margX.Sigma, 2/3))
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47
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48 margY = marginal_nodes(engines{e}, Y);
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49 assert(approxeq(margY.mu, 1))
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50 assert(approxeq(margY.Sigma, 2/3))
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51 end
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52
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