Fast sampling of Gaussian Markov random fields

Haavard Rue*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

222 Scopus citations

Abstract

This paper demonstrates how Gaussian Markov random fields (conditional autoregressions) can be sampled quickly by using numerical techniques for sparse matrices. The algorithm is general and efficient, and expands easily to various forms for conditional simulation and evaluation of normalization constants. We demonstrate its use by constructing efficient block updates in Markov chain Monte Carlo algorithms for disease mapping.

Original languageEnglish (US)
Pages (from-to)325-338
Number of pages14
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume63
Issue number2
StatePublished - Dec 1 2001

Keywords

  • Block sampling
  • Conditional autoregressive model
  • Divide-and-conquer strategy
  • Gaussian Markov random field
  • Markov chain Monte Carlo methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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