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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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+<html>
+<head>
+<title>
+Netlab Reference Manual demgp
+</title>
+</head>
+<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)
+
+
+</body>
+</html>
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