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Applied Mathematics and Statistics

News & Events

[Seminar] Hierarchical Bayesian Spatial Regression Models

AuthorApplied Mathematics & Statistics REG_DATE2023.10.19 Hits230

Speaker: Jaewoo Park   Location: B105
Date and Time: 

About the Speaker: Dr. Jaewoo Park
-  (Current) Assistant Professor, Department of Applied Statistics, Yonsei University
-  Ph.D. in Statistics, The Pennsylvania State University
-  BA in Applied Statistics, Economics, Yonsei University

Abstract
Spatial regressions are widely used to study relationships between spatial variables in many scientific domains, such as ecology, epidemiology, and sociology. For a given spatial latent process, hierarchical structures are defined for building complex high-dimensional models from simple and low-dimensional building blocks. Bayesian approaches are popular for hierarchical spatial models due to their computational and inferential advantages. In this talk, I will review the Bayesian framework for hierarchical spatial regression models. I will show how such models can be applied to real datasets, such as confirmed COVID-19 cases in the United States.