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/nethelp3.3/demard.htm | 51 ++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/demard.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/demard.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/demard.htm b/sourcecodes/bnt-master/nethelp3.3/demard.htm new file mode 100644 index 00000000..43923377 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/demard.htm @@ -0,0 +1,51 @@ + + + +Netlab Reference Manual demard + + + +

demard +

+Purpose +

+Automatic relevance determination using the MLP. + +

+Synopsis +

+demmlp1

+ + +

+Description +

+This script demonstrates the technique of automatic relevance +determination (ARD) using a synthetic problem having three input +variables: x1 is sampled uniformly from the range (0,1) and has +a low level of added Gaussian noise, x2 is a copy of x1 +with a higher level of added noise, and x3 is sampled randomly +from a Gaussian distribution. The single target variable is determined +by sin(2*pi*x1) with additive Gaussian noise. Thus x1 is +very relevant for determining the target value, x2 is of some +relevance, while x3 is irrelevant. The prior over weights is +given by the ARD Gaussian prior with a separate hyper-parameter for +the group of weights associated with each input. A multi-layer +perceptron is trained on this data, with re-estimation of the +hyper-parameters using evidence. The final values for the +hyper-parameters reflect the relative importance of the three inputs. + +

+See Also +

+demmlp1, demev1, mlp, evidence

+Pages: +Index +