Log_marginal_post: Natural Logorithm of the posterior.

View source: R/RcppExports.R

Log_marginal_postR Documentation

Natural Logorithm of the posterior.

Description

This function compute the natural Logorithm of the posterior assuming the GaSP or S-GaSP models for the discrepancy function.

Usage

Log_marginal_post(param, L_cur, output,  p_theta,  p_x, X, have_mean, CL, a, b,  cm_obs
,S_2_f,num_obs_all)

Arguments

param

Current parameters in the MCMC.

L_cur

Cholesky decomposition of the covariance matrix.

output

Experimental observations.

p_theta

Number of calibration parameters.

p_x

Number of range parameters.

X

Number of mean discrepancy parameters.

have_mean

Whether the mean discrepancy is zero or not.

CL

Prior parameter in the jointly robust prior.

a

Prior parameter in the jointly robust prior.

b

Prior parameter in the jointly robust prior.

cm_obs

Outputs from the mathematical model.

S_2_f

Variance of the data. This term is useful when there are repeated experiments.

num_obs_all

Total number of observations. If there is no repeated experiment, this is equal to the number of observable inputs.

Value

Natural logorithm of the posterior assuming the GaSP or S-GaSP models for the discrepancy function.

Author(s)

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>

References

A. O'Hagan and M. C. Kennedy (2001), Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology, 63, 425-464.

Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.

M. Gu and L. Wang (2017) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. arXiv preprint arXiv:1707.08215.

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


RobustCalibration documentation built on Sept. 19, 2022, 1:06 a.m.