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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual gpcovarp
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> gpcovarp
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Calculate the prior covariance for a Gaussian Process.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 covp = gpcovarp(net, x1, x2)
wolffd@0 20 [covp, covf] = gpcovarp(net, x1, x2)
wolffd@0 21 </PRE>
wolffd@0 22
wolffd@0 23
wolffd@0 24 <p><h2>
wolffd@0 25 Description
wolffd@0 26 </h2>
wolffd@0 27
wolffd@0 28 <p><CODE>covp = gpcovarp(net, x1, x2)</CODE> takes
wolffd@0 29 a Gaussian Process data structure <CODE>net</CODE> together with
wolffd@0 30 two matrices <CODE>x1</CODE> and <CODE>x2</CODE> of input vectors,
wolffd@0 31 and computes the matrix of the prior covariance. This is
wolffd@0 32 the function component of the covariance plus the exponential of the bias
wolffd@0 33 term.
wolffd@0 34
wolffd@0 35 <p><CODE>[covp, covf] = gpcovarp(net, x1, x2)</CODE> also returns the function
wolffd@0 36 component of the covariance.
wolffd@0 37
wolffd@0 38 <p><h2>
wolffd@0 39 See Also
wolffd@0 40 </h2>
wolffd@0 41 <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpcovarf.htm">gpcovarf</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr>
wolffd@0 42 <b>Pages:</b>
wolffd@0 43 <a href="index.htm">Index</a>
wolffd@0 44 <hr>
wolffd@0 45 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 46
wolffd@0 47
wolffd@0 48 </body>
wolffd@0 49 </html>