This function performs the posterior sampling for calibration parameters and other parameters in the model.
post_sample_MS(model,par_cur_theta, par_cur_individual, emulator,math_model_MS)
a S4 object of rcalibration_MS.
a list of current value of the posterior sample of calibration parameters.
a list of the current values of the posterior sample of the individual parameter of multiple sources.
a list of emulators if specified of multiple sources.
a list of mathematical models of multiple sources.
A list. The record_post is a vector of posterior values after burn-in samples. The record_theta is a matrix of of the posterior samples of theta after burn-in samples. The individual_par is a list where each element is a matrix of posterior samples of the range and nugget parameters for each source. The accept_S_theta is a vector where each element is the number of accepted posterior samples of calibration parameters. The accept_S_beta is a vector where each element is the number of accepted posterior samples of range and nugget parameters. The count_dec_record is vector where each element is the number of times the proposed samples of the calibration parameters are outside the range of the calibration parameters for each source.
Mengyang Gu [aut, cre]
Maintainer: Mengyang Gu <email@example.com>
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.
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