Bayesian computing with INLA: new features
Computation
2013-02-21 v2
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.
Cite
@article{arxiv.1210.0333,
title = {Bayesian computing with INLA: new features},
author = {Thiago G. Martins and Daniel Simpson and Finn Lindgren and Håvard Rue},
journal= {arXiv preprint arXiv:1210.0333},
year = {2013}
}