The derivative of natural logarithm of profile likelihood of the Robust GaSP model with regard to inverse range parameters and nugget-variance ratio parameter after plugging in the maximum likelihood estimator of the mean (trend) and variance parameters. When the nugget parameter is fixed, it only computes the derivative with regard to the inverse range parameter; otherwise it produces derivative with regard to inverse range parameter and nugget-variance ratio parameter.
log_profile_lik_deriv(param, nugget, nugget_est, R0, X, zero_mean,
output, kernel_type, alpha)
The numerical value of the derivative of natural logarithm of marginal likelihood with regard to range and nugget-variance ratio parameter (if not fixed). When the nugget is fixed, the derivative is on inverse-range parameters.
A vector of natural logarithm of inverse-range parameters and natural logarithm of the nugget-variance ratio parameter.
The nugget-variance ratio parameter if this parameter is fixed.
Boolean value of whether the nugget is estimated or fixed.
A list of matrix where the j-th matrix is an absolute difference matrix of the j-th input vector.
The mean basis function i.e. the trend function.
The mean basis function is zero or not.
The output vector.
A vector of integer specifying the type of kernels of each coordinate of the input.
In each coordinate of the vector, 1 means the pow_exp kernel with roughness parameter specified in alpha; 2 means matern_3_2 kernel; 3 means matern_5_2 kernel; 5 means periodic_gauss kernel; 5 means periodic_exp kernel.
Roughness parameters in the kernel functions.
tools:::Rd_package_author("RobustGaSP")
Maintainer: tools:::Rd_package_maintainer("RobustGaSP")
M. Gu. (2016). Robust uncertainty quantification and scalable computation for computer models with massive output. Ph.D. thesis. Duke University.
log_profile_lik.