gpLogLikeGradients: Compute the gradients for the parameters and X.

In alkalait/gptk: Gaussian Processes Tool-Kit

Description

computes the gradients of the Gaussian process log likelihood with respect to the model parameters (and optionally, as above with respect to inducing variables and input data) given the target data, input data and inducing variable locations.

Usage

gpLogLikeGradients( model, X=model$X, M, X_u, gX_u.return=FALSE,
                    gX.return=FALSE, g_beta.return=FALSE )

Arguments

model	the model structure for which gradients are computed.
X	the input data locations for which gradients are computed.
M	the scaled and bias removed target data for which the gradients are computed.
X_u	the inducing variable locations for which gradients are computed.
gX_u.return	(logical) return the gradient of the log likelihood with respect to the inducing variables. If inducing variables aren't being used this returns zero.
gX.return	(logical) return the gradient of the log likelihood with respect to the input data locations.
g_beta.return	(logical) to return the gradient of the log likelihood with respect to beta.

Value

gParam

contains the gradient of the log likelihood with respect to the model parameters (including any gradients with respect to beta).

See Also

gpLogLikelihood.

Examples

## missing

alkalait/gptk documentation built on March 7, 2020, 6:30 a.m.