predict_discrepancy_separable_2dim: Fast prediction when the test points lie on a 2D lattice.

Description Usage Arguments Value Author(s) References

Description

This function computes fast computation when the test points lie on a 2D lattice.

Usage

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predict_discrepancy_separable_2dim(object, testing_input_separable,
X_testing=matrix(0,length(testing_input_separable[[1]])*
length(testing_input_separable[[2]]),1), n_thinning=10,  
interval_est = NULL,math_model=NULL,...)

Arguments

object

an object of class rcalibration.

testing_input_separable

a list. The first element is a vector of the coordinate of the latitue and the second element is a vector of the coordinate of the longitude.

X_testing

a matrix of mean/trend for prediction.

n_thinning

number of points thinning the MCMC posterior samples.

math_model

a function for the math model to be calibrated.

Value

The returned value is a S4 CLass predictobj.rcalibration.

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.