Bayesian spatial modelling with R-INLA

Finn Lindgren*, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

388 Scopus citations

Abstract

The principles behind the interface to continuous domain spatial models in the R-INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and exible class of models ranging from (generalized) linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindström 2011), one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial models, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.

Original languageEnglish (US)
Pages (from-to)1-25
Number of pages25
JournalJournal of Statistical Software
Volume63
Issue number19
DOIs
StatePublished - Jan 1 2015

Keywords

  • Bayesian inference
  • Gaussian Markov random _elds
  • Laplace approximation
  • R
  • Stochastic partial differential equations

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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