WinBUGS is part of the BUGS project, which aims to make practical MCMC methods available to applied statisticians. WinBUGS can use either a standard ’point-and-click’ windows interface for controlling the analysis, or can construct the model using a graphical interface called DoodleBUGS. WinBUGS is a stand-alone program, although it can be called from other software.

References in zbMATH (referenced in 330 articles , 1 standard article )

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  1. Elfadaly, Fadlalla G.; Garthwaite, Paul H.: Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models (2017)
  2. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  3. Broemeling, Lyle D.: Bayesian methods for repeated measures (2016)
  4. Chan, Jennifer S.K.: Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches (2016)
  5. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  6. Dagne, Getachew A.: Bayesian segmental growth mixture Tobit models with skew distributions (2016)
  7. Frenklach, Michael; Packard, Andrew; Garcia-Donato, Gonzalo; Paulo, Rui; Sacks, Jerome: Comparison of statistical and deterministic frameworks of uncertainty quantification (2016)
  8. Gåsemyr, Jørund; Natvig, Bent; Tvete, Ingunn Fride: Estimating response ratios from continuous outcome data (2016)
  9. Jingjing Yang, Peng Ren: BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis (2016) arXiv
  10. Jones, Geoffrey; Johnson, Wesley O.: A Bayesian superpopulation approach to inference for finite populations based on imperfect diagnostic outcomes (2016)
  11. Levy, Roy; Mislevy, Robert J.: Bayesian psychometric modeling (2016)
  12. Liang, Yulan; Kelemen, Arpad: Bayesian state space models for dynamic genetic network construction across multiple tissues (2016)
  13. Lim, Kar Wai; Buntine, Wray; Chen, Changyou; Du, Lan: Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes (2016)
  14. Li, Yunxian; Tang, Niansheng; Jiang, Xuejun: Bayesian approaches for analyzing earthquake catastrophic risk (2016)
  15. Murray, Thomas A.; Hobbs, Brian P.; Sargent, Daniel J.; Carlin, Bradley P.: Flexible Bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effects (2016)
  16. Shaddick, Gavin; Zidek, James V.: Spatio-temporal methods in environmental epidemiology (2016)
  17. Shwartz, Michael; Burgess, James F.; Zhu, Joe: A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance (2016) ioport
  18. Verhagen, Josine; Levy, Roy; Millsap, Roger E.; Fox, Jean-Paul: Evaluating evidence for invariant items: a Bayes factor applied to testing measurement invariance in IRT models (2016)
  19. Wang, Dehui; Yang, Fan; Yang, Kai: Bayesian estimation for a geometric distribution with Logistic regressive structure and its application (2016)
  20. Banerjee, Sudipto; Carlin, Bradley P.; Gelfand, Alan E.: Hierarchical modeling and analysis for spatial data (2015)

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