KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/660710
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AbstractThe 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.
CitationVan Niekerk, J., Bakka, H., Rue, H., & Schenk, O. (2021). New Frontiers in Bayesian Modeling Using the INLA Package in R. Journal of Statistical Software, 100(2). doi:10.18637/jss.v100.i02
PublisherFoundation for Open Access Statistics
JournalJOURNAL OF STATISTICAL SOFTWARE
Except where otherwise noted, this item's license is described as Archived with thanks to JOURNAL OF STATISTICAL SOFTWARE. Article: Creative Commons Attribution License (CC-BY) Software: GPL General Public License version 2 or version 3 or a GPL-compatible license.