The function computes the derivative (with regard to log of inverse range parameter) of natural logarithm of marginal posterior density with the jointly robust prior prior after marginalizing out the mean (trend) and variance parameters by the location-scale prior.
neg_log_marginal_post_approx_ref_deriv(param, nugget, nugget.est,
R0, X, zero_mean,output, CL, a, b,
kernel_type, alpha)
The derivative of natural logarithm of marginal posterior density with jointly robust prior prior.
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.
Pseudoparameter in the approximate reference prior.
Pseudoparameter in the approximate reference prior.
Pseudoparameter in the approximate reference prior.
Type of kernel. matern_3_2 and matern_5_2 are Matern kernel with roughness parameter 3/2 and 5/2 respectively. pow_exp is power exponential kernel with roughness parameter alpha. If pow_exp is to be used, one needs to specify its roughness parameter alpha.
Roughness parameters in the kernel functions.
tools:::Rd_package_author("RobustGaSP")
Maintainer: tools:::Rd_package_maintainer("RobustGaSP")
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.
M. Gu (2018), Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection, arXiv:1804.09329.
neg_log_marginal_post_approx_ref.