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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 thetarget data is generated by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. Two Gaussian Processes, each with different covariance functionsare trained by optimising the hyperparameters using the scaled conjugate gradient algorithm. The final predictions areplotted 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>⌨️ 快捷键说明
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