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<html>
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<title>
Netlab Reference Manual demgp
</title>
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<body>
<H1> demgp
</H1>
<h2>
Purpose
</h2>
Demonstrate simple regression using a Gaussian Process.
<p><h2>
Synopsis
</h2>
<PRE>
demgp</PRE>
<p><h2>
Description
</h2>
The problem consists of one input variable <CODE>x</CODE> and one target variable
<CODE>t</CODE>. The values in <CODE>x</CODE> are chosen in two separated clusters and the
target data is generated by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian
noise. Two Gaussian Processes, each with different covariance functions
are trained by optimising the hyperparameters
using the scaled conjugate gradient algorithm. The final predictions are
plotted together with 2 standard deviation error bars.
<p><h2>
See Also
</h2>
<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>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
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