BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Getting started

Package details

AuthorDaniel Turek, Mark Risser
MaintainerDaniel Turek <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the BayesNSGP package in your browser

Any scripts or data that you put into this service are public.

BayesNSGP documentation built on Jan. 9, 2022, 9:07 a.m.