Visualization for Large-scale Gaussian Updates

Jonathan Rougier, Andrew Zammit Mangion

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
271 Downloads (Pure)

Abstract

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large-scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large-scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.
Original languageEnglish
Number of pages9
JournalScandinavian Journal of Statistics
Volume43
Early online date29 Jul 2016
DOIs
Publication statusPublished - 8 Nov 2016

Keywords

  • medal plot
  • spatial statistics
  • variance bound
  • variance update

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