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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual demhmc3
5 </title>
6 </head>
7 <body>
8 <H1> demhmc3
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 demhmc3</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 The problem consists of one input variable <CODE>x</CODE> and one target variable
26 <CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then
27 generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian
28 noise. The model is a 2-layer network with linear outputs, and the hybrid Monte
29 Carlo algorithm (with persistence) is used to sample from the posterior
30 distribution of the weights. The graph shows the underlying function,
31 300 samples from the function given by the posterior distribution of the
32 weights, and the average prediction (weighted by the posterior probabilities).
33
34 <p><h2>
35 See Also
36 </h2>
37 <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>
38 <b>Pages:</b>
39 <a href="index.htm">Index</a>
40 <hr>
41 <p>Copyright (c) Ian T Nabney (1996-9)
42
43
44 </body>
45 </html>