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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 demgp | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demgp | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Demonstrate simple regression using a Gaussian Process. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 demgp</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>. The values in <CODE>x</CODE> are chosen in two separated clusters and the | |
| 27 target data is generated by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian | |
| 28 noise. Two Gaussian Processes, each with different covariance functions | |
| 29 are trained by optimising the hyperparameters | |
| 30 using the scaled conjugate gradient algorithm. The final predictions are | |
| 31 plotted together with 2 standard deviation error bars. | |
| 32 | |
| 33 <p><h2> | |
| 34 See Also | |
| 35 </h2> | |
| 36 <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> | |
| 37 <b>Pages:</b> | |
| 38 <a href="index.htm">Index</a> | |
| 39 <hr> | |
| 40 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 41 | |
| 42 | |
| 43 </body> | |
| 44 </html> |