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author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
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Daniel@0 1 <html>
Daniel@0 2 <head>
Daniel@0 3 <title>
Daniel@0 4 Netlab Reference Manual demhmc2
Daniel@0 5 </title>
Daniel@0 6 </head>
Daniel@0 7 <body>
Daniel@0 8 <H1> demhmc2
Daniel@0 9 </H1>
Daniel@0 10 <h2>
Daniel@0 11 Purpose
Daniel@0 12 </h2>
Daniel@0 13 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
Daniel@0 14
Daniel@0 15 <p><h2>
Daniel@0 16 Synopsis
Daniel@0 17 </h2>
Daniel@0 18 <PRE>
Daniel@0 19 demhmc2</PRE>
Daniel@0 20
Daniel@0 21
Daniel@0 22 <p><h2>
Daniel@0 23 Description
Daniel@0 24 </h2>
Daniel@0 25 The problem consists of one input variable <CODE>x</CODE> and one target variable
Daniel@0 26 <CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then
Daniel@0 27 generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian
Daniel@0 28 noise. The model is a 2-layer network with linear outputs, and the hybrid Monte
Daniel@0 29 Carlo algorithm (without persistence) is used to sample from the posterior
Daniel@0 30 distribution of the weights. The graph shows the underlying function,
Daniel@0 31 100 samples from the function given by the posterior distribution of the
Daniel@0 32 weights, and the average prediction (weighted by the posterior probabilities).
Daniel@0 33
Daniel@0 34 <p><h2>
Daniel@0 35 See Also
Daniel@0 36 </h2>
Daniel@0 37 <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>
Daniel@0 38 <b>Pages:</b>
Daniel@0 39 <a href="index.htm">Index</a>
Daniel@0 40 <hr>
Daniel@0 41 <p>Copyright (c) Ian T Nabney (1996-9)
Daniel@0 42
Daniel@0 43
Daniel@0 44 </body>
Daniel@0 45 </html>