From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: BNW using Octave instead of Matlab. This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02 --- sourcecodes/bnt-master/docs/param_tieing.html | 47 +++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 sourcecodes/bnt-master/docs/param_tieing.html (limited to 'sourcecodes/bnt-master/docs/param_tieing.html') diff --git a/sourcecodes/bnt-master/docs/param_tieing.html b/sourcecodes/bnt-master/docs/param_tieing.html new file mode 100644 index 00000000..b426fe30 --- /dev/null +++ b/sourcecodes/bnt-master/docs/param_tieing.html @@ -0,0 +1,47 @@ + +
+
+
+
+Since nodes 2 and 3 in slice 2 (N7 and N8) +have different parents than their counterparts in slice 1 (N2 and N3), +they must be put into different equivalence classes. +Hence we define +
+eclass1 = [1 2 3 4 5]; +eclass2 = [1 6 7 4 5]; ++The dotted bubbles represent the equivalence classes. +Node 7 is the representative node for equivalence class +6, and node 8 is the rep. for class 7, so we need to write +
+bnet.CPD{6} = xxx_CPD(bnet, 7, xxx);
+bnet.CPD{7} = xxx_CPD(bnet, 8, xxx);
+
+In general, you can use the following code fragment:
+
+eclass = bnet.equiv_class(:);
+for e=1:max(eclass)
+ i = bnet.rep_of_eclass(e);
+ bnet.CPD{e} = xxx_CPD(bnet,i);
+end
+
+
--
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