Past, present and future of software for Bayesian inference

Štrumbelj, Erik (author), Bouchard-Côté, Alexandre (author), Corander, Jukka (author), Gelman, Andrew B. (author), Rue, Håvard (author), Murray, Lawrence (author), Pesonen, Henri (author), Plummer, Martyn (author), Vehtari, Aki (author)

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.

• Language: English
• Keywords: statistics, data analysis, MCMC, computation, probabilistic programming
• Work type: Article
• Typology: 1.02 - Review Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Author Accepted Manuscript
• Number of pages: Str. 46-61
• Numbering: Vol. 39, no. 1
• PID: 20.500.12556/RUL-183994
• UDC: 004.4:519.2
• ISSN on article: 0883-4237
• DOI: 10.1214/23-STS907
• COBISS.SI-ID: 186530307
• Publication date in RUL: 24.06.2026

Record is a part of a journal

• Title: Statistical science
• Shortened title: Stat. sci.
• Publisher: Institute of Mathematical Statistics
• ISSN: 0883-4237
• COBISS.SI-ID: 26447616

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: statistika, analiza podatkov, MCMC, računske metode, probabilistično programiranje

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0442
• Name: Podatkovne vede in digitalna preobrazba