A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology
Abstract
In this paper, we extend and analyze a Bayesian hierarchical spatio-temporal 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.
Cite
@article{arxiv.1811.08472,
title = {A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology},
author = {Giri Gopalan and Birgir Hrafnkelsson and Christopher K. Wikle and Håvard Rue and Guðfinna Aðalgeirsdóttir and Alexander H. Jarosch and Finnur Pálsson},
journal= {arXiv preprint arXiv:1811.08472},
year = {2019}
}
Comments
Revision accepted for publication by the Journal of Agricultural, Biological, and Environmental Statistics