Bayesian computing with INLA: new features

TL;DR

This paper introduces new features in the R-INLA package that extend its capabilities for Bayesian inference in latent Gaussian models, enhancing model scope and computational efficiency.

Contribution

The paper formalizes new developments in R-INLA, expanding the range of models it can analyze and improving hyperparameter marginal approximation methods.

Findings

Extended model scope with new R-INLA features

Efficient hyperparameter marginal approximation method

Maintained accuracy with reduced computational effort

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.