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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual demgpard
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> demgpard
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Demonstrate ARD using a Gaussian Process.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 demgpare</PRE>
wolffd@0 20
wolffd@0 21
wolffd@0 22 <p><h2>
wolffd@0 23 Description
wolffd@0 24 </h2>
wolffd@0 25 The data consists of three input variables <CODE>x1</CODE>, <CODE>x2</CODE> and
wolffd@0 26 <CODE>x3</CODE>, and one target variable
wolffd@0 27 <CODE>t</CODE>. The
wolffd@0 28 target data is generated by computing <CODE>sin(2*pi*x1)</CODE> and adding Gaussian
wolffd@0 29 noise, x2 is a copy of x1 with a higher level of added
wolffd@0 30 noise, and x3 is sampled randomly from a Gaussian distribution.
wolffd@0 31 A Gaussian Process, is
wolffd@0 32 trained by optimising the hyperparameters
wolffd@0 33 using the scaled conjugate gradient algorithm. The final values of the
wolffd@0 34 hyperparameters show that the model successfully identifies the importance
wolffd@0 35 of each input.
wolffd@0 36
wolffd@0 37 <p><h2>
wolffd@0 38 See Also
wolffd@0 39 </h2>
wolffd@0 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>
wolffd@0 41 <b>Pages:</b>
wolffd@0 42 <a href="index.htm">Index</a>
wolffd@0 43 <hr>
wolffd@0 44 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 45
wolffd@0 46
wolffd@0 47 </body>
wolffd@0 48 </html>