%0 Journal Article
%T Extending INLA to a class of near-Gaussian latent models
%A Thiago G. Martins
%A H£żvard Rue
%J Statistics
%D 2012
%I arXiv
%X This work extends the Integrated Nested Laplace Approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a near-Gaussian distribution. The proposed methodology is an essential component of a bigger project that aim to extend the R package INLA (R-INLA) in order to allow the user to add flexibility and challenge the Gaussian assumptions of some of the model components in a straightforward and intuitive way. Our approach is applied to two examples and the results are compared with that obtained by Markov Chain Monte Carlo (MCMC), showing similar accuracy with only a small fraction of computational time. Implementation of the proposed extension is available in the R-INLA package.
%U http://arxiv.org/abs/1210.1434v2