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 62 articles )

Showing results 1 to 20 of 62.
Sorted by year (citations)

1 2 3 4 next

  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  3. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  4. Molenaar, Dylan; de Boeck, Paul: Response mixture modeling: accounting for heterogeneity in item characteristics across response times (2018)
  5. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  6. Modarres, Mohammad; Amiri, Mehdi; Jackson, Christopher: Probabilistic physics of failure approach to reliability. Modeling, accelerated testing, prognosis and reliability assessment (2017)
  7. Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH
  8. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  9. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  10. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  11. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  12. Shaddick, Gavin; Zidek, James V.: Spatio-temporal methods in environmental epidemiology (2016)
  13. Andrew Finley; Sudipto Banerjee; Alan Gelfand: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (2015) not zbMATH
  14. Mahani, Alireza S.; Sharabiani, Mansour T. A.: SIMD parallel MCMC sampling with applications for big-data Bayesian analytics (2015)
  15. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  16. 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)
  17. Plummer, Martyn: Cuts in Bayesian graphical models (2015)
  18. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  19. Zhou, Wenjin; Rossetto, Allison M.: Finding protein thermostability and spin-coupling constant using Bayesian statistics (2015)
  20. Anders, R.; Oravecz, Z.; Batchelder, W. H.: Cultural consensus theory for continuous responses: a latent appraisal model for information pooling (2014)

1 2 3 4 next