Bayesian Heteroskedastic Gaussian Processes
Overview
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.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
Show 2 earlier changesHide 2 earlier changes
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 7includes a package overview page
- Examples that run
- 67%2 run in a plain check · 1 hold no code
- Exported functions with examples
- 67%
- Documented parameters
- 96%
- Return-value docs
- 100%
- Help pages citing references
- 57%
Downloads
Dependencies
Code & Tests
People & History
Package metadata
- First published
- 2025-07-14
- Total releases
- 3 / 1 yrs
- License
- LGPL
- Download size
- 62 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("bhetGP")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.