Bayesian computing with INLA: New features

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.