Open Access
February 2024 Past, Present and Future of Software for Bayesian Inference
Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, Aki Vehtari
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Statist. Sci. 39(1): 46-61 (February 2024). DOI: 10.1214/23-STS907


Software tools for Bayesian inference have undergone rapid evolution in the past three decades, following popularisation of the first generation MCMC-sampler implementations. More recently, exponential growth in the number of users has been stimulated both by the active development of new packages by the machine learning community and popularity of specialist software for particular applications. This review aims to summarize the most popular software and provide a useful map for a reader to navigate the world of Bayesian computation. We anticipate a vigorous continued development of algorithms and corresponding software in multiple research fields, such as probabilistic programming, likelihood-free inference and Bayesian neural networks, which will further broaden the possibilities for employing the Bayesian paradigm in exciting applications.

Funding Statement

Erik Štrumbelj’s work is partially funded by the Slovenian Research Agency (research core funding No. P2-0442). Andrew Gelman’s work is partially funded by the U.S. Office of Naval Research.


Special thanks to Christian Robert for the initiative and encouragement for this work.


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Erik Štrumbelj. Alexandre Bouchard-Côté. Jukka Corander. Andrew Gelman. Håvard Rue. Lawrence Murray. Henri Pesonen. Martyn Plummer. Aki Vehtari. "Past, Present and Future of Software for Bayesian Inference." Statist. Sci. 39 (1) 46 - 61, February 2024.


Published: February 2024
First available in Project Euclid: 18 February 2024

MathSciNet: MR4718526
Digital Object Identifier: 10.1214/23-STS907

Keywords: computation , data analysis , MCMC , probabilistic programming , statistics

Rights: Copyright © 2024 Institute of Mathematical Statistics

Vol.39 • No. 1 • February 2024
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