New frontiers in Bayesian modeling using the INLA package in R
Additional information
Authors
van Niekerk J.,
Bakka H.,
Rue H. .,
Schenk O.
Type
Journal Article
Year
2021
Language
English
Abstract
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.
Journal
Journal of Statistical Software
Volume
100
Number
2
Start page number
1
End page number
28
Keywords
INLA, joint model, non-separable, spatial, temporal, R
