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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual demgp
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> demgp
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Demonstrate simple regression 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 demgp</PRE>
wolffd@0 20
wolffd@0 21
wolffd@0 22 <p><h2>
wolffd@0 23 Description
wolffd@0 24 </h2>
wolffd@0 25 The problem consists of one input variable <CODE>x</CODE> and one target variable
wolffd@0 26 <CODE>t</CODE>. The values in <CODE>x</CODE> are chosen in two separated clusters and the
wolffd@0 27 target data is generated by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian
wolffd@0 28 noise. Two Gaussian Processes, each with different covariance functions
wolffd@0 29 are trained by optimising the hyperparameters
wolffd@0 30 using the scaled conjugate gradient algorithm. The final predictions are
wolffd@0 31 plotted together with 2 standard deviation error bars.
wolffd@0 32
wolffd@0 33 <p><h2>
wolffd@0 34 See Also
wolffd@0 35 </h2>
wolffd@0 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>
wolffd@0 37 <b>Pages:</b>
wolffd@0 38 <a href="index.htm">Index</a>
wolffd@0 39 <hr>
wolffd@0 40 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 41
wolffd@0 42
wolffd@0 43 </body>
wolffd@0 44 </html>