Description Usage Arguments Value Author(s) References Examples
This covariance function is defined as: k(x^p,x^q) = sf2 * exp( t(x^p  x^q)*inv(P)*(x^p  x^q)/2) , in which where the P matrix is ell^2 times the unit matrix and sf2 is the signal variance. The hyperparameter vector is loghyper = [ log(ell) ,log(sqrt(sf2)) ]
1 
loghyper 

x 

z 

testset.covariances 

If z
is not null and testset.covariances
is TRUE this function calculates test set covariances and if its FALSE the function computes derivative matrix.
When covNoise is called without parameters is reports the minimum number of parameters other than loghyper which it can accept.
The output of this function is a list consisting variables A and B. B will include testset covariances calculation when testset.covariances
is TRUE.
Afshin Sadeghi
Carl Edward Rasmussen and Christopher K. I. Williams.Gaussian Processes for Machine Learning. MIT Press, 2006. ISBN 026218253X. Carl Edward Rasmussen & Hannes Nickisch. gpml(GAUSSIAN PROCESS REGRESSION AND CLASSIFICATION Toolbox) Matlab Library.
1 2  params= covSEiso()
params

[1] 2
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