Bayesian computing with INLA: New features

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

Research output: Contribution to journalArticlepeer-review

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 & data analysis
Volume67
Early online date2 May 2013
DOIs
Publication statusPublished - Nov 2013

Keywords

  • Approximate Bayesian inference
  • INLA
  • Latent Gaussian models
  • stat.CO

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