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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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