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The BUGS project: Evolution, critique and future directions

Statistics in Medicine · 2009 · Vol. 28(25) · pp. 3049–3067

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.

Statistical Methods and Bayesian InferenceBayesian Methods and Mixture ModelsGaussian Processes and Bayesian InferenceComputer scienceFlexibility (engineering)SoftwarePrincipal (computer security)Gibbs samplingBayesian probabilityInferenceRaising (metalworking)Bayesian inferenceSoftware bug

MeSH terms

Bayes TheoremData Interpretation, StatisticalSoftwareModels, Statistical

Funding

  • Economic and Social Research Council
Citations
1,882
FWCI
70.94
field-weighted impact
References
66
Percentile
100%
vs. same field & year
Citations per year
References
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence · 1984 · 17,882 citations
Equation of State Calculations by Fast Computing Machines
The Journal of Chemical Physics · 1953 · 36,613 citations
Sampling-Based Approaches to Calculating Marginal Densities
Journal of the American Statistical Association · 1990 · 6,639 citations
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