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Activity Number: 131 - Methods for Spatial, Temporal, and Spatio-Temporal Data
Type: Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #318512
Title: Ordered Conditional Approximation of Potts Models
Author(s): Anirban Chakraborty* and Matthias Katzfuss
Companies: Texas A&M University and Texas A&M University
Keywords: spatial statistics; satellite data; Vecchia approximation; hidden Potts model; conditional distribution; mage processing

Potts models, which can be used to analyze dependent observations on a spatial field, have seen widespread application in a variety of areas, including statistical mechanics, neuroscience, and quantum computing. However, because of the involvement of an in-tractable normalizing constant, inference for Potts model is computationally expensive for large spatial fields. We propose ordered conditional approximations that enable fast evaluation of Potts likelihoods and rapid inference on hidden Potts fields. The computational complexity of our approximation methods is linear in the number of spatial locations. We illustrate the advantages of our approach using simulated data and real observations.

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

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