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