Think continuous: Markovian Gaussian models in spatial statistics

Daniel Simpson*, Finn Lindgren, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spatial statistics. Unfortunately, it has traditionally been difficult to link GMRFs with the more traditional Gaussian random field models, as the Markov property is difficult to deploy in continuous space. Following the pioneering work of Lindgren etal. (2011), we expound on the link between Markovian Gaussian random fields and GMRFs. In particular, we discuss the theoretical and practical aspects of fast computation with continuously specified Markovian Gaussian random fields, as well as the clear advantages they offer in terms of clear, parsimonious, and interpretable models of anisotropy and non-stationarity.

Original languageEnglish (US)
Pages (from-to)16-29
Number of pages14
JournalSpatial Statistics
Volume1
DOIs
StatePublished - May 1 2012

Keywords

  • Bayesian inference
  • Gaussian Markov random fields
  • Gaussian fields
  • Geo-statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law

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