OpenBUGS

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 74 articles )

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  1. Bhattacharjee, Atanu: Bayesian approaches in oncology using R and OpenBUGS (2021)
  2. 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
  3. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  4. Baer, Daniel R.; Lawson, Andrew B.: Evaluation of Bayesian multiple stage estimation under spatial CAR model variants (2019)
  5. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  6. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  7. Molenaar, Dylan; de Boeck, Paul: Response mixture modeling: accounting for heterogeneity in item characteristics across response times (2018)
  8. Onicescu, Georgiana; Lawson, Andrew B.; Zhang, Jiajia; Gebregziabher, Mulugeta; Wallace, Kristin; Eberth, Jan M.: Spatially explicit survival modeling for small area cancer data (2018)
  9. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  10. Modarres, Mohammad; Amiri, Mehdi; Jackson, Christopher: Probabilistic physics of failure approach to reliability. Modeling, accelerated testing, prognosis and reliability assessment (2017)
  11. Musal, Rasim M.; Ekin, Tahir: Medical overpayment estimation: a Bayesian approach (2017)
  12. Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH
  13. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  14. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  15. Achcar, Jorge A.; Lopes, Sílvia R. C.: Linear and non-linear regression models assuminga stable distribution (2016)
  16. Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T. A. Sharabiani: Stochastic Newton Sampler: The R Package sns (2016) not zbMATH
  17. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  18. Fonseca, Rodney Vasconcelos; Nobre, Juvêncio Santos; Farias, Rafael Bráz Azevedo: Comparative inference and diagnostic in a reparametrized Birnbaum-Saunders regression model (2016)
  19. Härkänen, Tommi; Karvanen, Juha; Tolonen, Hanna; Lehtonen, Risto; Djerf, Kari; Juntunen, Teppo; Koskinen, Seppo: Systematic handling of missing data in complex study designs -- experiences from the health 2000 and 2011 surveys (2016)
  20. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)

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