log_ref_marginal_post_ppgasp: Natural logarithm of reference marginal posterior density of...

In RobustGaSP: Robust Gaussian Stochastic Process Emulation

Natural logarithm of reference marginal posterior density of the PP GaSP model

Description

This function computes the natural logarithm of marginal posterior density of the PP GaSP model with reference prior of inverse range parameter (beta parameterization) after marginalizing out the mean (trend) and variance parameters by the location-scale prior.

Usage

log_ref_marginal_post_ppgasp(param, nugget, nugget_est, R0, X, zero_mean,
output, kernel_type, alpha)

Arguments

param	a vector of natural logarithm of inverse-range parameters and natural logarithm of the nugget-variance ratio parameter.
nugget	the nugget-variance ratio parameter if this parameter is fixed.
nugget_est	Boolean value of whether the nugget is estimated or fixed.
R0	a list of matrix where the j-th matrix is an absolute difference matrix of the j-th input vector.
X	the mean basis function i.e. the trend function.
zero_mean	the mean basis function is zero or not.
output	the output matrix.
kernel_type	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.
alpha	roughness parameters in the kernel functions.

Value

The natural logarithm of marginal posterior density with reference prior of inverse range parameter (beta parameterization).

Author(s)

Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]

Maintainer: Mengyang Gu <[e-mail]>

References

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.

RobustGaSP documentation built on June 1, 2022, 9:08 a.m.