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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 addednoise, and x3 is sampled randomly from a Gaussian distribution.A Gaussian Process, istrained by optimising the hyperparameters using the scaled conjugate gradient algorithm. The final values of thehyperparameters show that the model successfully identifies the importanceof 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>⌨️ 快捷键说明
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