Mercurial > hg > camir-aes2014
diff 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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_incinerator_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +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