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Title: Modeling Multivariate Spatial Dependencies Using Graphical Models
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.  more » « less
Award ID(s):
1915803
NSF-PAR ID:
10469218
Author(s) / Creator(s):
; ;
Publisher / Repository:
The New England Statistical Society (NESS)
Date Published:
Journal Name:
The New England Journal of Statistics in Data Science
ISSN:
2693-7166
Page Range / eLocation ID:
283 to 295
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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