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The New England Journal of Statistics in Data Science


Modeling Multivariate Spatial Dependencies Using Graphical Models
Volume 1, Issue 2 (2023), pp. 283–295
Pub. online: 6 September 2023      Type: Spatial And Environmental Statistics      Open accessOpen Access
Accepted
30 May 2023
Published
6 September 2023

Abstract

Graphical models have witnessed significant growth and usage in spatial data science for modeling data referenced over a massive number of spatial-temporal coordinates. Much of this literature has focused on a single or relatively few spatially dependent outcomes. Recent attention has focused upon addressing modeling and inference for substantially large number of outcomes. While spatial factor models and multivariate basis expansions occupy a prominent place in this domain, this article elucidates a recent approach, graphical Gaussian Processes, that exploits the notion of conditional independence among a very large number of spatial processes to build scalable graphical models for fully model-based Bayesian analysis of multivariate spatial data.

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© 2023 New England Statistical Society
Open access article under the CC BY license.

Keywords
Bayesian inference Covariance selection Graphical models Graphical Gaussian Process Multivariate dependencies Spatial process models

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