The BUGS project: evolution, critique and future directions
Abstract
BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives.
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Bayesian (or multiparameter) evidence synthesis is a calibration approach developed in health technology assessment by using Markov chain Monte Carlo via freely available WinBUGS software.
“Bayesian (or multiparameter) evidence synthesis is a calibration approach developed in health technology assessment by using Markov chain Monte Carlo via freely available WinBUGS software [16].”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [16]
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References 65 · Cited by 1,876
- Authors
- Lunn, D., Spiegelhalter, D., Thomas, A., & Best, N.
- Affiliations
- MRC Biostatistics Unit (GB)
- University of St Andrews (GB)
- Imperial College London (GB)
- Corresponding author
- Lunn, David
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2009-10-13
- Volume
- 28
- Issue
- 25
- Pages
- 3049-3067
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2134012062
- Cited by
- 1,876
- References
- 65
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 33 · 2023: 67 · 2022: 93 · 2021: 126 · 2020: 120 · 2019: 142 · 2018: 145 · 2017: 137 · 2016: 161 · 2015: 159 · 2014: 215 · 2013: 187 · 2012: 149
- FWCI
- 66.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (U.1052.00.005)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Keywords
- Bayesian inference, Gibbs sampling, Bayesian modelling, software development history, software limitations, statistical software development
- MeSH terms
- Bayes Theorem, Data Interpretation, Statistical, Software, Models, Statistical
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