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 446 articles , 1 standard article )

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  1. Broemeling, Lyle D.: Bayesian inference for stochastic processes (2018)
  2. Kim, Hea-Jung: Bayesian hierarchical robust factor analysis models for partially observed sample-selection data (2018)
  3. Lindqvist, Bo H.; Taraldsen, Gunnar: On the proper treatment of improper distributions (2018)
  4. Alvares, Danilo; Armero, Carmen; Forte, Anabel; Chopin, Nicolas: Sequential Monte Carlo methods in random intercept models for longitudinal data (2017)
  5. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Mayoral, Asunción; Morales, Javier; Barber, Antoni: Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices (2017)
  6. Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017)
  7. 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)
  8. Culpepper, Ryan; Cobb, Andrew: Contextual equivalence for probabilistic programs with continuous random variables and scoring (2017)
  9. Elfadaly, Fadlalla G.; Garthwaite, Paul H.: Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models (2017)
  10. Erosheva, Elena A.; Curtis, S.McKay: Dealing with reflection invariance in Bayesian factor analysis (2017)
  11. Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
  12. Huang, Yangxin; Lu, Tao: Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features (2017)
  13. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017)
  14. Riebler, Andrea; Held, Leonhard: Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations (2017)
  15. Sebastian Meyer and Leonhard Held and Michael Höhle: Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance (2017)
  16. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  17. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  18. Yu, Yan; Wu, Chaojiang; Zhang, Yuankun: Penalised spline estimation for generalised partially linear single-index models (2017)
  19. 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)
  20. Bhadra, Anindya; Carroll, Raymond J.: Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems (2016)

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