New Frontiers in Bayesian Modeling Using the INLA Package in R
Dublin Core
Title
New Frontiers in Bayesian Modeling Using the INLA Package in R
Subject
INLA, joint model, non-separable, spatial, temporal, R
Description
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
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
Creator
Janet van Niekerk
Source
https://www.jstatsoft.org/article/view/v100i02
Publisher
King Abdullah University
of Science and Technology
of Science and Technology
Date
November 2021
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Janet van Niekerk, “New Frontiers in Bayesian Modeling Using the INLA Package in R,” Repository Horizon University Indonesia, accessed March 13, 2025, https://repository.horizon.ac.id/items/show/8215.