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 402 articles , 1 standard article )

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  1. Alvares, Danilo; Armero, Carmen; Forte, Anabel; Chopin, Nicolas: Sequential Monte Carlo methods in random intercept models for longitudinal data (2017)
  2. Cho, Sun-Joo; Goodwin, Amanda P.: Modeling learning in doubly multilevel binary longitudinal data using generalized linear mixed models: an application to measuring and explaining word learning (2017)
  3. Culpepper, Ryan; Cobb, Andrew: Contextual equivalence for probabilistic programs with continuous random variables and scoring (2017)
  4. Elfadaly, Fadlalla G.; Garthwaite, Paul H.: Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models (2017)
  5. Huang, Yangxin; Lu, Tao: Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features (2017)
  6. Riebler, Andrea; Held, Leonhard: Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations (2017)
  7. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  8. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  9. Yu, Yan; Wu, Chaojiang; Zhang, Yuankun: Penalised spline estimation for generalised partially linear single-index models (2017)
  10. Arcuti, Simona; Pollice, Alessio; Ribecco, Nunziata; D’Onghia, Gianfranco: Bayesian spatiotemporal analysis of zero-inflated biological population density data by a delta-normal spatiotemporal additive model (2016)
  11. Bhadra, Anindya; Carroll, Raymond J.: Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems (2016)
  12. Broemeling, Lyle D.: Bayesian methods for repeated measures (2016)
  13. Chan, Jennifer S.K.: Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches (2016)
  14. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  15. Dagne, Getachew A.: Bayesian segmental growth mixture Tobit models with skew distributions (2016)
  16. Frenklach, Michael; Packard, Andrew; Garcia-Donato, Gonzalo; Paulo, Rui; Sacks, Jerome: Comparison of statistical and deterministic frameworks of uncertainty quantification (2016)
  17. Gagnon, Jacob; Liang, Hua; Liu, Anna: Spherical radial approximation for nested mixed effects models (2016)
  18. Gåsemyr, Jørund; Natvig, Bent; Tvete, Ingunn Fride: Estimating response ratios from continuous outcome data (2016)
  19. He, Lei; He, Daojiang; Cao, Mingxiang: Objective Bayesian analysis of degradation model with respect to a Wiener process (2016)
  20. Jingjing Yang, Peng Ren: BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis (2016) arXiv

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