This function compute the natural Logorithm of the posterior assuming no discrepancy function.
Log_marginal_post_no_discrepancy(param, output, p_theta, X, have_mean,
inv_output_weights, cm_obs,S_2_f,num_obs_all)Natural logorithm of the posterior assuming no discrepancy function.
Current parameters in the MCMC.
Experimental observations.
Number of calibration parameters.
Number of mean discrepancy parameters.
Whether the mean discrepancy is zero or not.
Inverse of the weights of the outputs
Outputs from the mathematical model.
Variance of the data. This term is useful when there are repeated experiments.
Total number of observations. If there is no repeated experiment, this is equal to the number of observable inputs.
tools:::Rd_package_author("RobustCalibration")
Maintainer: tools:::Rd_package_maintainer("RobustCalibration")
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.