Bayesian computing with INLA: new features

Thiago G. Martins, Daniel Simpson, Finn Lindgren, Håvard Rue

Research output: Contribution to journalArticleResearchpeer-review

228 Citations (Scopus)

Abstract

The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. New developments in the R-INLA are formalized and it is shown how these features greatly extend the scope of models that can be analyzed by this interface. The current default method in R-INLA to approximate the posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparameters, without any need for numerical integration, is discussed.

Original languageEnglish
Pages (from-to)68-83
Number of pages16
JournalComputational Statistics and Data Analysis
Volume67
DOIs
Publication statusPublished - Nov 2013
Externally publishedYes

Keywords

  • Approximate
  • Bayesian inference
  • INLA
  • Latent Gaussian models

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