neg_log_marginal_post_ref_ppgasp: Negative natural logarithm of reference marginal posterior...

In RobustGaSP: Robust Gaussian Stochastic Process Emulation

Negative natural logarithm of reference marginal posterior density of the PP GaSP model with regard to a specific parameterization.

Description

Negative natural logarithm of marginal posterior density (with regard to a specific parameterization) 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

neg_log_marginal_post_ref_ppgasp(param, nugget, nugget.est
,R0, X, zero_mean,output, prior_choice,kernel_type, alpha)

Arguments

param	a vector of natural logarithm of inverse-range parameters and natural logarithm of the noise-variance ratio parameter.
nugget	the noise-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.
prior_choice	parameterization: ref_xi for log inverse range parameterization or ref_gamma for range parameterization.
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 negative natural logarithm of marginal posterior density with reference prior with regard to a specific 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.

M. Gu (2018), Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection, arXiv:1804.09329.

RobustGaSP documentation built on May 29, 2024, 1:26 a.m.