New frontiers in Bayesian modeling using the INLA package in R

van Niekerk, Janet; Bakka, Haakon; Rue, HÃ¥vard; Schenk, Olaf
The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It is a non-sampling based framework which provides approximate results for Bayesian inference, using sparse matrices. The swift uptake of this framework for Bayesian modeling is rooted in the computational efficiency of the approach and catalyzed by the demand presented by the big data era. In this paper, we present new developments within the INLA package with the aim to provide a computationally efficient mechanism for the Bayesian inference of relevant challenging situations.
Type of Publication:
Journal of Statistical Software
1 - 27
Hits: 19


logo cscs

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Read more