Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/nethelp3.3/demhmc2.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demhmc2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual demhmc2 +</title> +</head> +<body> +<H1> demhmc2 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. + +<p><h2> +Synopsis +</h2> +<PRE> +demhmc2</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 (without persistence) is used to sample from the posterior +distribution of the weights. The graph shows the underlying function, +100 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="demhmc3.htm">demhmc3</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> \ No newline at end of file