WinBUGS

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 364 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. Huang, Yangxin; Lu, Tao: Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features (2017)
  3. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  4. Yu, Yan; Wu, Chaojiang; Zhang, Yuankun: Penalised spline estimation for generalised partially linear single-index models (2017)
  5. Bhadra, Anindya; Carroll, Raymond J.: Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems (2016)
  6. Broemeling, Lyle D.: Bayesian methods for repeated measures (2016)
  7. Chan, Jennifer S.K.: Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches (2016)
  8. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  9. Dagne, Getachew A.: Bayesian segmental growth mixture Tobit models with skew distributions (2016)
  10. Frenklach, Michael; Packard, Andrew; Garcia-Donato, Gonzalo; Paulo, Rui; Sacks, Jerome: Comparison of statistical and deterministic frameworks of uncertainty quantification (2016)
  11. Gagnon, Jacob; Liang, Hua; Liu, Anna: Spherical radial approximation for nested mixed effects models (2016)
  12. Gåsemyr, Jørund; Natvig, Bent; Tvete, Ingunn Fride: Estimating response ratios from continuous outcome data (2016)
  13. Jingjing Yang, Peng Ren: BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis (2016) arXiv
  14. Jones, Geoffrey; Johnson, Wesley O.: A Bayesian superpopulation approach to inference for finite populations based on imperfect diagnostic outcomes (2016)
  15. Levy, Roy; Mislevy, Robert J.: Bayesian psychometric modeling (2016)
  16. Liang, Yulan; Kelemen, Arpad: Bayesian state space models for dynamic genetic network construction across multiple tissues (2016)
  17. Lim, Kar Wai; Buntine, Wray; Chen, Changyou; Du, Lan: Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes (2016)
  18. Li, Yunxian; Tang, Niansheng; Jiang, Xuejun: Bayesian approaches for analyzing earthquake catastrophic risk (2016)
  19. 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)
  20. Ortega, Francisco J.; Gavilan, Jose M.: Bayesian estimation of the half-normal regression model with deterministic frontier (2016)

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