New Frontiers in Bayesian Modeling Using the INLA Package in R

Janet Van Niekerk, Haakon Bakka, Haavard Rue, Olaf Schenk

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1-28
Number of pages28
JournalJournal of Statistical Software
Issue number2
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2022-01-27

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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