comparison toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_incinerator_bnet.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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