Improving performances of MCMC for Nearest Neighbor Gaussian Process models with full data augmentation

Sébastien Coube-Sisqueille and Benoît Liquet

Computational Statistics & Data Analysis, 2022, vol. 168, issue C

Abstract: Even though Nearest Neighbor Gaussian Processes (NNGP) alleviate MCMC implementation of Bayesian space-time models considerably, they do not solve the convergence problems caused by high model dimension. Frugal alternatives such as response or collapsed algorithms are one answer. An alternative approach is to keep full data augmentation, but to try and make it more efficient. Two strategies are presented.

Keywords: Nearest Neighbor Gaussian Process; Space-time models; Chromatic sampler; Interweaving (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:168:y:2022:i:c:s0167947321002024

DOI: 10.1016/j.csda.2021.107368

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