This function computes fast computation when the test points lie on a 2D lattice for multiple sources of observations.
predict_separable_2dim_MS(object, testing_input_separable,
X_testing=NULL, math_model=NULL,...)The returned value is a S4 CLass predictobj.rcalibration_MS.
an object of class rcalibration.
a list of two. In the first (outer) list, each list is a source of test input. Then in the second (interior) 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.
mean/trend for prediction where the defaul value is NULL.
a list of functions of math models to be calibrated.
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 (2018) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. SIAM/ASA Journal on Uncertainty Quantification, 6, 1555-1583.
M. Gu (2019) Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection . Bayesian Analysis, 14, 857-885.