Log_marginal_post {RobustCalibration} | R 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.