Log_marginal_post_no_discrepancy: Natural Logorithm of the posterior with no discrepancy...

Description Usage Arguments Value Author(s) References

Description

This function compute the natural Logorithm of the posterior assuming no discrepancy function.

Usage

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Log_marginal_post_no_discrepancy(param,  output,  p_theta,   X, have_mean, 
inv_output_weights,  cm_obs)

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

Inverse of the weights of the outputs

cm_obs

Outputs from the mathematical model.

Value

Natural logorithm of the posterior assuming no discrepancy function.

Author(s)

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <mgu6@jhu.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 May 2, 2019, 9:36 a.m.