Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 124 - Algorithms for Threat Detection
Type: Topic-Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #317134
Title: Matérn Gaussian Fields on Graphs: Theory and Applications
Author(s): Daniel Sanz-Alonso and Ruiyi Yang*
Companies: University of Chicago and University of Chicago
Keywords: Matérn Gaussian fields; Latent Gaussian models; graph-Laplacians
Abstract:

In this talk we will consider Matérn Gaussian fields on graphs, derived from a graphical approximation of the stochastic partial differential equation representation of the usual Matérn Gaussian fields on Euclidean domains. We formalize a convergence analysis under a manifold assumption. The graph Matérn Gaussian fields have sparse precision matrices that allow fast sampling and inference. We demonstrate through examples their applications in spatial statistics, Bayesian inverse problems and graph-based machine learning. We will see that the graph representations facilitates exchange of ideas across these disciplines.


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

Back to the full JSM 2021 program