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first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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> |