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Bayesian computing with INLA: New features

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

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.

Suggested Citation

  • Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
    • Handle: RePEc:eee:csdana:v:67:y:2013:i:c:p:68-83
    DOI: 10.1016/j.csda.2013.04.014
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    References listed on IDEAS

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