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Activity Number: 91 - Spatial Statistics and UQ: Foundations for Innovation in Environmental Science
Type: Invited
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #320699
Title: Hierarchical, Nonstationary, Spatial Modeling to Account for Model Error in Computational Hurricane Models
Author(s): David Higdon*
Companies: Virginia Tech
Keywords: deep Gaussian process; hierarchical model; spatial statistics; Bayesian computation

It has been noted that deep machine learning models are closely connected to spatial hierarchical models (e.g. Wikle (2019)). In both approaches, complex spatial fields are developed through dependence on simpler spatial fields. The dependence is commonly additive in hierarchical modeling settings but can be more general in a deep ML model. In this talk we explore the use of deep Gaussian processes in settings where a hierarchical spatial model seems appropriate. We'll explore the basic deep GP formulation and computing required to carry out a fully Bayesian analysis and compare this to a more standard hierarchical model. We'll use an example from model-based prediction of rainfall from tropical cyclones hitting the east coast of the U.S.

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

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