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+<html>
+<head>
+<title>
+Netlab Reference Manual demgpard
+</title>
+</head>
+<body>
+<H1> demgpard
+</H1>
+<h2>
+Purpose
+</h2>
+Demonstrate ARD using a Gaussian Process.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+demgpare</PRE>
+
+
+<p><h2>
+Description
+</h2>
+The data consists of three input variables <CODE>x1</CODE>, <CODE>x2</CODE> and
+<CODE>x3</CODE>, and one target variable 
+<CODE>t</CODE>. The 
+target data is generated by computing <CODE>sin(2*pi*x1)</CODE> and adding Gaussian 
+noise, x2 is a copy of x1 with a higher level of added
+noise, and x3 is sampled randomly from a Gaussian distribution.
+A Gaussian Process, is
+trained by optimising the hyperparameters 
+using the scaled conjugate gradient algorithm. The final values of the
+hyperparameters show that the model successfully identifies the importance
+of each input. 
+
+<p><h2>
+See Also
+</h2>
+<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>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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