Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 267 - Statistical Methods for Large Spatial Data
Type: Invited
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistical Computing
Abstract #317015
Title: Graphical Gaussian Processes for Highly Multivariate Spatial Data
Author(s): Abhirup Datta*
Companies: Johns Hopkins University
Keywords:
Abstract:

We propose multivariate Graphical Gaussian Processes (GGP) using a novel construction called "stitching" to directly incorporate graphical models into cross-covariance functions ensuring process-level conditional independence among variables. For the Mat\'ern family of functions, stitching yields a multivariate GGP whose univariate components are exactly Mat\'ern GPs over the entire domain, and that conforms to process-level conditional independence as specified by the graphical model. For highly multivariate settings and decomposable graphical models, stitching offers massive gains both in terms of parameter dimensionality and computations. Using simulation experiments we demonstrate the utility of the graphical GP with a Mat\'ern covariance to jointly model spatial data on many variables. We conclude with an application to air-pollution modelling.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program