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A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology

Author

Listed:
  • 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)

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.

Suggested Citation

  • Giri Gopalan & Birgir Hrafnkelsson & Christopher K. Wikle & Håvard Rue & Guðfinna Aðalgeirsdóttir & Alexander H. Jarosch & Finnur Pálsson, 2019. "A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 669-692, December.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:4:d:10.1007_s13253-019-00367-1
    DOI: 10.1007/s13253-019-00367-1
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    References listed on IDEAS

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    Cited by:

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