A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology

• Activity Number: 162 - SBSS Student Paper Award Session - I
• Type: Topic Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Giri Gopalan* and Birgir Hrafnkelsson and Christopher K. Wikle and Håvard Rue and Guðfinna Th Aðalgeirsdóttir and Alexander H. Jarosch and Finnur Pálsson
• Companies: University of Iceland and University of Iceland and University of Missouri and King Abdullah University of Science and Technology and University of Iceland and University of Innsbruck and University of Iceland
• Keywords: model discrepancy; uncertainty quantification; emulation

Abstract

The objective of this talk is to describe a Bayesian hierarchical spatio-temporal statistical model developed in the context of glaciology, which is extensible to other physical systems. A feature of this model is to represent the discrepancy between the output of a computer simulator and the real physical process with a multivariate random walk. In addition to the model, I will present a few ways to make Bayesian inference computationally feasible. In particular, these ways include using bandwidth limited linear-algebraic routines, the use of an approximation to the likelihood, and the use of first-order emulation of a computer simulator (e.g., a numerical partial differential equation solver). The model assumptions, in addition to the speed and accuracy of computational methods used, are assessed using a test system from glaciology; as such, a discussion of Icelandic glaciers and the physics of glaciers will also be included.