Vecchia approximations of Gaussian-process predictions

TL;DR

This paper develops and analyzes Vecchia approximation methods for Gaussian process predictions, enabling fast, accurate spatial predictions at large datasets with improved uncertainty quantification.

Contribution

It introduces a general Vecchia framework for GP predictions, providing new methods with linear computational complexity and practical recommendations for different applications.

Findings

Methods are faster or more accurate than existing approaches.

New approaches reduce artifacts in prediction maps.

Theoretical and numerical analysis confirms linear complexity.

Abstract

Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasible for large datasets. Vecchia approximations of GPs have been used to enable fast evaluation of the likelihood for parameter inference. Here, we study Vecchia approximations of spatial predictions at observed and unobserved locations, including obtaining joint predictive distributions at large sets of locations. We consider a general Vecchia framework for GP predictions, which contains some novel and some existing special cases. We study the accuracy and computational properties of these approaches theoretically and numerically, proving that our new methods exhibit linear computational complexity in the total number of spatial locations. We show that certain choices within the framework can have a strong effect on uncertainty quantification and computational cost, which leads to specific recommendations on which methods are most suitable for various settings. We also apply our methods to a satellite dataset of chlorophyll fluorescence, showing that the new methods are faster or more accurate than existing methods, and reduce unrealistic artifacts in prediction maps.