wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual gpcovar wolffd@0: wolffd@0: wolffd@0: wolffd@0:

gpcovar wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Calculate the covariance for a Gaussian Process. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: cov = gpcovar(net, x)
wolffd@0: [cov, covf] = gpcovar(net, x)
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wolffd@0: Description wolffd@0:

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cov = gpcovar(net, x) takes wolffd@0: a Gaussian Process data structure net together with wolffd@0: a matrix x of input vectors, and computes the covariance wolffd@0: matrix cov. The inverse of this matrix is used when calculating wolffd@0: the mean and variance of the predictions made by net. wolffd@0: wolffd@0:

[cov, covf] = gpcovar(net, x) also generates the covariance wolffd@0: matrix due to the covariance function specified by net.covarfn wolffd@0: as calculated by gpcovarf. wolffd@0: wolffd@0:

wolffd@0: Example wolffd@0:

wolffd@0: In the following example, the inverse covariance matrix is calculated wolffd@0: for a set of training inputs x and is then wolffd@0: passed to gpfwd so that predictions (with mean ytest and wolffd@0: variance sigsq) can be made for the test inputs wolffd@0: xtest. wolffd@0:

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wolffd@0: cninv = inv(gpcovar(net, x)); 
wolffd@0: [ytest, sigsq] = gpfwd(net, xtest, cninv);
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wolffd@0: See Also wolffd@0:

wolffd@0: gp, gppak, gpunpak, gpcovarp, gpcovarf, gpfwd, gperr, gpgrad

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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