A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology

  • Giri GopalanEmail author
  • Birgir Hrafnkelsson
  • Christopher K. Wikle
  • Håvard Rue
  • Guðfinna Aðalgeirsdóttir
  • Alexander H. Jarosch
  • Finnur Pálsson


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.


Model discrepancy Uncertainty quantification Emulation 



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Copyright information

© International Biometric Society 2019

Authors and Affiliations

  • Giri Gopalan
    • 1
    Email author
  • Birgir Hrafnkelsson
    • 1
  • Christopher K. Wikle
    • 2
  • Håvard Rue
    • 3
  • Guðfinna Aðalgeirsdóttir
    • 1
  • Alexander H. Jarosch
    • 4
  • Finnur Pálsson
    • 1
  1. 1.University of IcelandReykjavíkIceland
  2. 2.University of MissouriColumbiaUSA
  3. 3.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  4. 4.University of InnsbruckInnsbruckAustria

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