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| author | Daniel Wolff |
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| date | Fri, 19 Aug 2016 13:07:06 +0200 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual demhmc2 | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demhmc2 | |
| 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 demhmc2</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 (without persistence) is used to sample from the posterior | |
| 30 distribution of the weights. The graph shows the underlying function, | |
| 31 100 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="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> | |
| 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> |