Bayesian computing with INLA: New features

• DOI: 10.1016/j.csda.2013.04.014
• Publication Date: 2013-05-02T21:05:08Z
• Authors: Thiago G. Martins; Daniel Simpson; Finn Lindgren; Håvard Rue
• Keywords: stat.CO; FOS: Computer and information sciences; 0301 basic medicine; 03 medical and health sciences; Approximate Bayesian inference; Latent Gaussian models; INLA; 0101 mathematics; Statistics - Computation; 01 natural sciences; Computation (stat.CO)

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.