Natural logarithm of marginal posterior density with the jointly robust prior of inverse range parameter of the PP GaSP model after marginalizing out the mean (trend) and variance parameters by the location-scale prior.
neg_log_marginal_post_approx_ref_ppgasp(param, nugget,
nugget.est, R0, X, zero_mean,output, CL, a, b,kernel_type, alpha)
The natural logarithm of the marginal posterior density with the jointly robust prior prior of inverse range parameter (beta parameterization).
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.
a matrix where each row is one runs of the computer model output.
prior parameters in the approximate reference prior.
prior parameter in the approximate reference prior.
prior parameter in the approximate reference prior.
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. and J.O. Berger (2016). Parallel partial Gaussian process emulation for computer models with massive output. Annals of Applied Statistics, 10(3), 1317-1347.
M. 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.