Bayesian Geostatistics Using Predictive Stacking

TL;DR

This paper introduces a Bayesian predictive stacking approach for geostatistical models that efficiently combines model inferences across hyper-parameters, enabling accurate spatial predictions without intensive MCMC computations.

Contribution

The authors develop a novel Bayesian stacking method for geostatistics that is computationally efficient and theoretically justified, avoiding iterative sampling methods.

Findings

Stacked inference performs comparably to full Bayesian sampling.

Method significantly reduces computational cost.

Theoretical analysis supports the validity of the approach.

Abstract

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.