BayesGP - Efficient Implementation of Gaussian Process in Bayesian
Hierarchical Models
Implements Bayesian hierarchical models with flexible
Gaussian process priors, focusing on Extended Latent Gaussian
Models and incorporating various Gaussian process priors for
Bayesian smoothing. Computations leverage finite element
approximations and adaptive quadrature for efficient inference.
Methods are detailed in Zhang, Stringer, Brown, and Stafford
(2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown,
and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang,
Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and
Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.
