# Bayesian Geostatistics Using Predictive Stacking

arXiv: https://arxiv.org/abs/2304.12414 · HTML: https://mlsanity.com/paper/2304.12414/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2023.

## Built on by (most cited first, 3 of 3)

- [Bayesian Data Sketching for Varying Coefficient Regression Models](https://mlsanity.com/paper/2506.00270/) (2025)
- [SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations](https://mlsanity.com/paper/2511.00685/) (2025)
- [Amortized Bayesian inference for actigraph time sheet data from mobile devices](https://mlsanity.com/paper/2602.20611/) (2026)

## Abstract (arXiv)

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.