Sample_delta: Sample the model discrepancy.

View source: R/RcppExports.R

Sample_deltaR Documentation

Sample the model discrepancy.

Description

This function samples a vector of the model discrepancy for the scenario with multiple sources and measurement bias.

Usage

Sample_delta(cov_inv_all,  tilde_output_cur,   param,  p_x,   
num_sources, num_obs, rand_norm)

Arguments

cov_inv_all

a list of inverse covariances of discrepancy and measurement bias.

tilde_output_cur

a list of transformed observations.

param

a list of the current parameters values in MCMC.

p_x

a list of dimensions of the observable inputs.

num_sources

the number of sources.

num_obs

the number of observations.

rand_norm

the vector of i.i.d. standard normal samples.

Value

A vector of samples of model discrepancy.

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.


RobustCalibration documentation built on Sept. 8, 2023, 5:23 p.m.