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| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual gpcovarp | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> gpcovarp | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Calculate the prior covariance for a Gaussian Process. | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 covp = gpcovarp(net, x1, x2) | |
| 20 [covp, covf] = gpcovarp(net, x1, x2) | |
| 21 </PRE> | |
| 22 | |
| 23 | |
| 24 <p><h2> | |
| 25 Description | |
| 26 </h2> | |
| 27 | |
| 28 <p><CODE>covp = gpcovarp(net, x1, x2)</CODE> takes | |
| 29 a Gaussian Process data structure <CODE>net</CODE> together with | |
| 30 two matrices <CODE>x1</CODE> and <CODE>x2</CODE> of input vectors, | |
| 31 and computes the matrix of the prior covariance. This is | |
| 32 the function component of the covariance plus the exponential of the bias | |
| 33 term. | |
| 34 | |
| 35 <p><CODE>[covp, covf] = gpcovarp(net, x1, x2)</CODE> also returns the function | |
| 36 component of the covariance. | |
| 37 | |
| 38 <p><h2> | |
| 39 See Also | |
| 40 </h2> | |
| 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> | |
| 42 <b>Pages:</b> | |
| 43 <a href="index.htm">Index</a> | |
| 44 <hr> | |
| 45 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 46 | |
| 47 | |
| 48 </body> | |
| 49 </html> |