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view toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_incinerator_bnet.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function bnet = mk_incinerator_bnet(ns) % MK_INCINERATOR_BNET The waste incinerator emissions example from Cowell et al p145 % function bnet = mk_incinerator_bnet(ns) % % If ns is omitted, we use the scalars and binary nodes and the original params. % Otherwise, we use random params of the desired size. % % Lauritzen, "Propogation of Probabilities, Means and Variances in Mixed Graphical Association Models", % JASA 87(420): 1098--1108 % This example is reprinted on p145 of "Probabilistic Networks and Expert Systems", % Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer. % For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#cg_model % node numbers F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; names = {'F', 'W', 'E', 'B', 'C', 'D', 'Min', 'Mout', 'L'}; n = 9; dnodes = [F W B]; cnodes = mysetdiff(1:n, dnodes); % node sizes - all cts nodes are scalar, all discrete nodes are binary if nargin < 1 ns = ones(1, n); ns(dnodes) = 2; rnd = 0; else rnd = 1; end % topology (p 1099, fig 1) dag = zeros(n); dag(F,E)=1; dag(W,[E Min D]) = 1; dag(E,D)=1; dag(B,[C D])=1; dag(D,[L Mout])=1; dag(Min,Mout)=1; % params (p 1102) bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'names', names); if rnd for i=dnodes(:)' bnet.CPD{i} = tabular_CPD(bnet, i); end for i=cnodes(:)' bnet.CPD{i} = gaussian_CPD(bnet, i); end else bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.85 0.15]); % 1=stable, 2=unstable bnet.CPD{F} = tabular_CPD(bnet, F, 'CPT', [0.95 0.05]); % 1=intact, 2=defect bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [2/7 5/7]); % 1=industrial, 2=household bnet.CPD{E} = gaussian_CPD(bnet, E, 'mean', [-3.9 -0.4 -3.2 -0.5], ... 'cov', [0.00002 0.0001 0.00002 0.0001]); bnet.CPD{D} = gaussian_CPD(bnet, D, 'mean', [6.5 6.0 7.5 7.0], ... 'cov', [0.03 0.04 0.1 0.1], 'weights', [1 1 1 1]); bnet.CPD{C} = gaussian_CPD(bnet, C, 'mean', [-2 -1], 'cov', [0.1 0.3]); bnet.CPD{L} = gaussian_CPD(bnet, L, 'mean', 3, 'cov', 0.25, 'weights', -0.5); bnet.CPD{Min} = gaussian_CPD(bnet, Min, 'mean', [0.5 -0.5], 'cov', [0.01 0.005]); bnet.CPD{Mout} = gaussian_CPD(bnet, Mout, 'mean', 0, 'cov', 0.002, 'weights', [1 1]); end