# Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R

@inproceedings{Risser2019BayesianNG, title={Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R}, author={Mark D. Risser and Daniel Turek}, year={2019} }

In spite of the diverse literature on nonstationary Gaussian process modeling, the software for implementing convolution-based methods is extremely limited, particularly for fully Bayesian analysis. To address this gap, here we present the BayesNSGP software package for R that enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. Our approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying…

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