Bayesian computing with INLA: New features

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

Computational Statistics & Data Analysis, 2013, vol. 67, issue C, 68-83

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.

Keywords: Approximate Bayesian inference; INLA; Latent Gaussian models (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (43)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:67:y:2013:i:c:p:68-83

DOI: 10.1016/j.csda.2013.04.014

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