The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. The project began in 1989 in the MRC Biostatistics Unit, Cambridge, and led initially to the `Classic’ BUGS program, and then onto the WinBUGS software developed jointly with the Imperial College School of Medicine at St Mary’s, London. Development is now focussed on the OpenBUGS project.

References in zbMATH (referenced in 190 articles )

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  1. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  2. Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
  3. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  4. Chen, Hsiang-Chun; Wehrly, Thomas E.: Approximate uniform shrinkage prior for a multivariate generalized linear mixed model (2016)
  5. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  6. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  7. Chiu, Chia-Yi; Köhn, Hans-Friedrich; Zheng, Yi; Henson, Robert: Joint maximum likelihood estimation for diagnostic classification models (2016)
  8. De Haan-Rietdijk, Silvia; Gottman, John M.; Bergeman, Cindy S.; Hamaker, Ellen L.: Get over it! A multilevel threshold autoregressive model for state-dependent affect regulation (2016)
  9. Dienes, Zoltan: How Bayes factors change scientific practice (2016)
  10. Kliber, Agata: The leverage effect puzzle: the case of European sovereign credit default swap market (2016)
  11. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  12. Ricardo Oliveros-Ramos, Yunne-Jai Shin: Calibrar: an R package for fitting complex ecological models (2016) arXiv
  13. Shang, Han Lin: A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data (2016)
  14. Turliuc, Calin Rares; Dickens, Luke; Russo, Alessandra; Broda, Krysia: Probabilistic abductive logic programming using Dirichlet priors (2016)
  15. Anders, Royce; Batchelder, William H.: Cultural consensus theory for the ordinal data case (2015)
  16. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  17. Fried, Roland; Agueusop, Inoncent; Bornkamp, Björn; Fokianos, Konstantinos; Fruth, Jana; Ickstadt, Katja: Retrospective Bayesian outlier detection in INGARCH series (2015)
  18. Fu, Shuai: A hierarchical Bayesian approach to negative binomial regression (2015)
  19. Gill, Jeff: Bayesian methods. A social and behavioral sciences approach (2015)
  20. Liu, Ying; Gelman, Andrew; Zheng, Tian: Simulation-efficient shortest probability intervals (2015)

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