Past, Present, and Future of Software for Bayesian Inference

Erik Štrumbelj; Alexandre Bouchard-Côté; Jukka Corander; Andrew Gelman; Haavard Rue; Lawrence Murray; Henri Pesonen; Martyn Plummer; Aki Vehtari

Past, Present, and Future of Software for Bayesian Inference

Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Haavard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, Aki Vehtari

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English (US)
Journal	Accepted by Statistical Science
State	Published - Sep 19 2023

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https://repository.kaust.edu.sa/bitstream/10754/694575/1/Bayesian_software_review-7.pdf

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title = "Past, Present, and Future of Software for Bayesian Inference",

abstract = "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.",

author = "Erik {\v S}trumbelj and Alexandre Bouchard-C{\^o}t{\'e} and Jukka Corander and Andrew Gelman and Haavard Rue and Lawrence Murray and Henri Pesonen and Martyn Plummer and Aki Vehtari",

note = "KAUST Repository Item: Exported on 2023-09-21 Acknowledgements: Erik {\v S}trumbelj{\textquoteright}s work is partially funded by the Slovenian Research Agency (research core funding No. P2- 0442). Andrew Gelman{\textquoteright}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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month = sep,

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language = "English (US)",

journal = "Accepted by Statistical Science",

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T1 - Past, Present, and Future of Software for Bayesian Inference

AU - Štrumbelj, Erik

AU - Bouchard-Côté, Alexandre

AU - Corander, Jukka

AU - Gelman, Andrew

AU - Rue, Haavard

AU - Murray, Lawrence

AU - Pesonen, Henri

AU - Plummer, Martyn

AU - Vehtari, Aki

N1 - KAUST Repository Item: Exported on 2023-09-21 Acknowledgements: 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.

PY - 2023/9/19

Y1 - 2023/9/19

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/694575

M3 - Article

JO - Accepted by Statistical Science

JF - Accepted by Statistical Science

ER -

Past, Present, and Future of Software for Bayesian Inference

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