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arXiv · 1210.0333

Bayesian computing with INLA: new features

Thiago G. Martins·Daniel Simpson·Finn Lindgren·Håvard Rue
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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. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate 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.

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Thiago G. Martins, Daniel Simpson, Finn Lindgren, Håvard Rue. 2013-02-20. Bayesian computing with INLA: new features. https://arxiv.org/abs/1210.0333

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