logLikAdditiveGrad: Gradient of the Log-Likelihood of a Additive Gaussian...

In lineqGPR: Gaussian Process Regression Models with Linear Inequality Constraints

Description

Compute the gradient of the negative log-likelihood of an Additive Gaussian Process.

Usage

logLikAdditiveGrad(
  par = unlist(purrr::map(model$kernParam, "par")),
  model,
  parfixed = rep(FALSE, model$d * length(par)),
  mcmc.opts = NULL,
  estim.varnoise = FALSE
)

Arguments

par	the values of the covariance parameters.
model	an object with "lineqAGP" S3 class.
parfixed	indices of fixed parameters to do not be optimised.
mcmc.opts	not used.
estim.varnoise	If true, a noise variance is estimated.

Value

the gradient of the negative log-likelihood.

Author(s)

A. F. Lopez-Lopera.

References

Rasmussen, C. E. and Williams, C. K. I. (2005), "Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)". The MIT Press. [link]

See Also

logLikAdditiveFun

lineqGPR documentation built on Jan. 11, 2020, 9:23 a.m.