A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology

Giri Gopalan (), Birgir Hrafnkelsson, Christopher K. Wikle, Håvard Rue, Guðfinna Aðalgeirsdóttir, Alexander H. Jarosch and Finnur Pálsson
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Giri Gopalan: University of Iceland
Birgir Hrafnkelsson: University of Iceland
Christopher K. Wikle: University of Missouri
Håvard Rue: King Abdullah University of Science and Technology
Guðfinna Aðalgeirsdóttir: University of Iceland
Alexander H. Jarosch: University of Innsbruck
Finnur Pálsson: University of Iceland

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 4, No 7, 669-692

Abstract: Abstract In this paper, we extend and analyze a Bayesian hierarchical spatiotemporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate random walk. For computational efficiency, linear algebra for bandwidth limited matrices is utilized, and first-order emulator inference allows for the fast emulation of a numerical partial differential equation (PDE) solver. A test scenario from a physical system motivated by glaciology is used to examine the speed and accuracy of the computational methods used, in addition to the viability of modeling assumptions. We conclude by discussing how the model and associated methodology can be applied in other physical contexts besides glaciology.

Keywords: Model discrepancy; Uncertainty quantification; Emulation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-019-00367-1

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