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Activity Number: 144 - Advances in Climate Informatics
Type: Invited
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics and the Environment
Abstract #309459
Title: Fast Computing for Hierarchical Spatial Models, with an Application to Ice Sheet Models
Author(s): Murali Haran and Ben Seiyon Lee*
Companies: Penn State University and Pennsylvania State University
Keywords: spatial statistics; hierarchical models; Gaussian random fields; ice sheets; dimension reduction
Abstract:

Hierarchical spatial models are very widely used in a number of disciplines, including climate science. They provide a framework for combining information from disparate sources while accounting for spatial dependence and complicated error structures. Computation can remain a challenge for many such models, especially when the data sets are high-dimensional. For example, research on the West Antarctic ice sheet requires combining physical models with non-Gaussian spatial data. I will describe a new dimension-reduction strategy for speeding up Bayesian inference for hierarchical spatial models, and demonstrate how it is useful for studying ice sheets.


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

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