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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/demhmc3.htm
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
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
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+<html>
+<head>
+<title>
+Netlab Reference Manual demhmc3
+</title>
+</head>
+<body>
+<H1> demhmc3
+</H1>
+<h2>
+Purpose
+</h2>
+Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+demhmc3</PRE>
+
+
+<p><h2>
+Description
+</h2>
+The problem consists of one input variable <CODE>x</CODE> and one target variable 
+<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then 
+generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian 
+noise. The model is a 2-layer network with linear outputs, and the hybrid Monte
+Carlo algorithm (with persistence) is used to sample from the posterior
+distribution of the weights.  The graph shows the underlying function,
+300 samples from the function given by the posterior distribution of the
+weights, and the average prediction (weighted by the posterior probabilities).
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="demhmc2.htm">demhmc2</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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