2012/10/04 by Thiago G. Martins, Martins, Thiago G., Rue, Håvard
Computer Science · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.1210.1434
openalex publication_date 2012/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.