Sample_sigma_2_theta_m_no_discrepancy: Sample the variance and mean parameters with no discrepancy...

In RobustCalibration: Robust Calibration of Imperfect Mathematical Models

Sample the variance and mean parameters with no discrepancy function.

Description

This function samples the variance and mean parameters assuming no discrepancy function.

Usage

Sample_sigma_2_theta_m_no_discrepancy(param,  output,   p_theta,   
                                      X,  have_mean, inv_output_weights, cm_obs,
                                      S_2_f,num_obs_all)

Arguments

param	Current parameters in the MCMC.
output	Experimental observations.
p_theta	Number of calibration parameters.
X	Number of mean discrepancy parameters.
have_mean	Whether the mean discrepancy is zero or not.
inv_output_weights	The inverse of the weights of outputs..
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

A vector of samples of the variance and trend parameters.

Author(s)

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <[e-mail]>

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 June 22, 2024, 10:37 a.m.