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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual demgpard
5 </title>
6 </head>
7 <body>
8 <H1> demgpard
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Demonstrate ARD using a Gaussian Process.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 demgpare</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 The data consists of three input variables <CODE>x1</CODE>, <CODE>x2</CODE> and
26 <CODE>x3</CODE>, and one target variable
27 <CODE>t</CODE>. The
28 target data is generated by computing <CODE>sin(2*pi*x1)</CODE> and adding Gaussian
29 noise, x2 is a copy of x1 with a higher level of added
30 noise, and x3 is sampled randomly from a Gaussian distribution.
31 A Gaussian Process, is
32 trained by optimising the hyperparameters
33 using the scaled conjugate gradient algorithm. The final values of the
34 hyperparameters show that the model successfully identifies the importance
35 of each input.
36
37 <p><h2>
38 See Also
39 </h2>
40 <CODE><a href="demgp.htm">demgp</a></CODE>, <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>
41 <b>Pages:</b>
42 <a href="index.htm">Index</a>
43 <hr>
44 <p>Copyright (c) Ian T Nabney (1996-9)
45
46
47 </body>
48 </html>