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 40 articles )

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  1. Modarres, Mohammad; Amiri, Mehdi; Jackson, Christopher: Probabilistic physics of failure approach to reliability. Modeling, accelerated testing, prognosis and reliability assessment (2017)
  2. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  3. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  4. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  5. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  6. Shaddick, Gavin; Zidek, James V.: Spatio-temporal methods in environmental epidemiology (2016)
  7. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  8. Musoro, Jammbe Z.; Geskus, Ronald B.; Zwinderman, Aeilko H.: A joint model for repeated events of different types and multiple longitudinal outcomes with application to a follow-up study of patients after kidney transplant (2015)
  9. Plummer, Martyn: Cuts in Bayesian graphical models (2015)
  10. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  11. Zhou, Wenjin; Rossetto, Allison M.: Finding protein thermostability and spin-coupling constant using Bayesian statistics (2015)
  12. Anders, R.; Oravecz, Z.; Batchelder, W.H.: Cultural consensus theory for continuous responses: a latent appraisal model for information pooling (2014)
  13. Choi, Jiin; Anderson, Stewart J.; Richards, Thomas J.; Thompson, Wesley K.: Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data (2014)
  14. Josse, Julie; van Eeuwijk, Fred; Piepho, Hans-Peter; Denis, Jean-Baptiste: Another look at Bayesian analysis of AMMI models for genotype-environment data (2014)
  15. Thompson, John: Bayesian analysis with Stata (2014)
  16. Cowles, Mary Kathryn: Applied Bayesian statistics. With R and OpenBUGS examples. (2013)
  17. DeCarlo, Lawrence T.: Signal detection models for the same-different task (2013)
  18. Higgs, Megan D.; Link, William A.; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.: Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year (2013)
  19. Ranta, Jukka; Mikkelä, Antti; Tuominen, Pirkko; Wahlström, Helene: Bayesian risk assessment for salmonella in egg laying flocks under zero apparent prevalence and dynamic test sensitivity (2013)
  20. Cancho, Vicente G.; de Castro, Mário; Rodrigues, Josemar: A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model (2012)

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