bhetGP-package {bhetGP}R Documentation

Package bhetGP

Description

Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) <doi:10.1080/10618600.2018.1458625>) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), <doi:10.1080/10618600.2022.2129662>). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood.

Important Functions

Author(s)

Parul V. Patil parulvijay@vt.edu

References

M. Binois, Robert B. Gramacy, M. Ludkovski (2018), Practical heteroskedastic Gaussian process modeling for large simulation experiments, Journal of Computational and Graphical Statistics, 27(4), 808–821.

Katzfuss, Matthias, Joseph Guinness, and Earl Lawrence. Scaled Vecchia approximation for fast computer-model emulation. SIAM/ASA Journal on Uncertainty Quantification 10.2 (2022): 537-554.

Sauer, A., Cooper, A., & Gramacy, R. B. (2023). Vecchia-approximated deep Gaussian processes for computer experiments. Journal of Computational and Graphical Statistics, 32(3), 824-837.

Examples

# See ?bhetGP, or ?bhomGP for examples


[Package bhetGP version 1.0.1 Index]