BUGS is a software package for performing Bayesian inference Using Gibbs Sampling. The user specifies a statistical model, of (almost) arbitrary complexity, by simply stating the relationships between related variables. The software includes an ‘expert system’, which determines an appropriate MCMC (Markov chain Monte Carlo) scheme (based on the Gibbs sampler) for analysing the specified model. The user then controls the execution of the scheme and is free to choose from a wide range of output types. There are two main versions of BUGS, namely WinBUGS and OpenBUGS. This site is dedicated to OpenBUGS, an open-source version of the package, on which all future development work will be focused. OpenBUGS, therefore, represents the future of the BUGS project. WinBUGS, on the other hand, is an established and stable, stand-alone version of the software, which will remain available but not further developed. The latest versions of OpenBUGS (from v3.0.7 onwards) have been designed to be at least as efficient and reliable as WinBUGS over a wide range of test applications. Please see here for more information on WinBUGS. OpenBUGS runs on x86 machines with MS Windows, Unix/Linux or Macintosh (using Wine).

References in zbMATH (referenced in 84 articles )

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  1. Jreich, Rana; Hatte, Christine; Parent, Eric: Review of Bayesian selection methods for categorical predictors using JAGS (2022)
  2. Salmerón, Diego: Bayesian beta nonlinear models with constrained parameters to describe ruminal degradation kinetics (2022)
  3. Bhattacharjee, Atanu: Bayesian approaches in oncology using R and OpenBUGS (2021)
  4. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  5. Mayrink, V. D., Duarte, J. D. N., Demarqui, F. N.: pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution (2021) not zbMATH
  6. Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., Nguyen, D.: Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages (2021) not zbMATH
  7. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  8. Farzammehr, Mohadeseh Alsadat; Zadkarami, Mohammad Reza; McLachlan, Geoffrey J.; Lee, Sharon X.: Skew-normal Bayesian spatial heterogeneity panel data models (2020)
  9. Timothy D. Meehan, Nicole L. Michel, Håvard Rue: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R (2020) not zbMATH
  10. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  11. Baer, Daniel R.; Lawson, Andrew B.: Evaluation of Bayesian multiple stage estimation under spatial CAR model variants (2019)
  12. Singh, Sukhdev; Tripathi, Yogesh Mani; Wu, Shuo-Jye: Bayesian analysis for lognormal distribution under progressive type-II censoring (2019)
  13. Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn; Lewis, Paul O.: Partition weighted approach for estimating the marginal posterior density with applications (2019)
  14. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  15. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  16. Molenaar, Dylan; de Boeck, Paul: Response mixture modeling: accounting for heterogeneity in item characteristics across response times (2018)
  17. Onicescu, Georgiana; Lawson, Andrew B.; Zhang, Jiajia; Gebregziabher, Mulugeta; Wallace, Kristin; Eberth, Jan M.: Spatially explicit survival modeling for small area cancer data (2018)
  18. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  19. Modarres, Mohammad; Amiri, Mehdi; Jackson, Christopher: Probabilistic physics of failure approach to reliability. Modeling, accelerated testing, prognosis and reliability assessment (2017)
  20. Musal, Rasim M.; Ekin, Tahir: Medical overpayment estimation: a Bayesian approach (2017)

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