gpCovGrads: Sparse objective function gradients wrt Covariance functions...

In gptk: Gaussian Processes Tool-Kit

Description

gives the gradients of the log likelihood with respect to the components of the sparse covariance (or the full covariance for the ftc case).

Usage

gpCovGrads(model, M)

Arguments

model	the model for which the gradients are to be computed.
M	The training data for which the computation is to be made

Value

gK_uu	the gradient of the likelihood with respect to the elements of K_uu (or in the case of the 'ftc' criterion the gradients with respect to the kernel).
gK_uf	the gradient of the likelihood with respect to the elements of K_uf.
gLambda	the gradient of the likelihood with respect to the diagonal term in the fitc approximation and the blocks of the pitc approximation.
gBeta	the gradient with respect to the beta term in the covariance structure.

See Also

gpCreate, gpLogLikeGradients.

Examples

## missing

Example output

Loading required package: Matrix
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.1-1 (2017-07-02) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps

gptk documentation built on May 2, 2019, 3:27 p.m.