Bayesian Geostatistics Using Predictive Stacking

• 成果类型: Article
• 署名作者: Zhang, Lu; Tang, Wenpin; Banerjee, Sudipto
• 署名单位: University of Southern California; Columbia University; University of California System; University of California Los Angeles
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2566449
• 发表日期: 2026-04-03
• 页码: 1549-1561
• 关键词: Bayesian inference; Gaussian Processes; geostatistics; stacking; Spatial Data; inference; parameter

摘要

We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the range of values of the hyper-parameters. We devise stacking of means and posterior densities in a manner that is computationally efficient without resorting to iterative algorithms such as Markov chain Monte Carlo (MCMC) and can exploit the benefits of parallel computations. We offer novel theoretical insights into the resulting inference within an infill asymptotic paradigm and through empirical results showing that stacked inference is comparable to full sampling-based Bayesian inference at a significantly lower computational cost. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.