Bayesian computing with INLA: New features

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 featu...

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Bibliographic Details
Published inComputational statistics & data analysis Vol. 67; pp. 68 - 83
Main Authors Martins, Thiago G., Simpson, Daniel, Lindgren, Finn, Rue, Håvard
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2013
Subjects
Approximate Bayesian inference
Approximation
INLA
Latent Gaussian models
INLA
Latent Gaussian models
Approximate Bayesian inference
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2013.04.014