A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology

Giri Gopalan, Birgir Hrafnkelsson, Christopher K. Wikle, Haavard Rue, Guðfinna Aðalgeirsdóttir, Alexander H. Jarosch, Finnur Pálsson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)669-692
Number of pages24
JournalJournal of Agricultural, Biological and Environmental Statistics
Issue number4
StatePublished - Jun 12 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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