The derivative of natural logarithm of marginal likelihood of the Robust GaSP model with regard to inverse range parameters and nugget-variance ratio parameter after marginalizing out the mean (trend) and variance parameters the location-scale prior. When the nugget 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_marginal_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_marginal_lik.