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

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  • Coube-Sisqueille, Sébastien
  • Liquet, Benoît

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.

Suggested Citation

  • Coube-Sisqueille, Sébastien & Liquet, Benoît, 2022. "Improving performances of MCMC for Nearest Neighbor Gaussian Process models with full data augmentation," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:csdana:v:168:y:2022:i:c:s0167947321002024
    DOI: 10.1016/j.csda.2021.107368
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    References listed on IDEAS

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