BUGS

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 364 articles )

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  1. Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.: Automatic differentiation variational inference (2017)
  2. Lázaro, Elena; Armero, Carmen; Rubio, Luis: Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems (2017)
  3. Ligtvoet, Rudy: Exact one-sided Bayes factors for 2 by 2 contingency tables (2017)
  4. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  5. Men, Zhongxian; Mcleish, Don; Kolkiewicz, Adam W.; Wirjanto, Tony S.: Comparison of asymmetric stochastic volatility models under different correlation structures (2017)
  6. Muff, Stefanie; Ott, Manuela; Braun, Julia; Held, Leonhard: Bayesian two-component measurement error modelling for survival analysis using INLA -- a case study on cardiovascular disease mortality in Switzerland (2017)
  7. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017) not zbMATH
  8. Rikhtehgaran, Reyhaneh: An application of Dirichlet process in clustering subjects via variance shift models: a course-evaluation study (2017)
  9. Rodríguez-Picón, Luis Alberto; Flores-Ochoa, Víctor Hugo; Méndez-González, Luis Carlos; Rodríguez-Medina, Manuel Arnoldo: Bivariate degradation modelling with marginal heterogeneous stochastic processes (2017)
  10. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  11. Turek, Daniel; de Valpine, Perry; Paciorek, Christopher J.; Anderson-Bergman, Clifford: Automated parameter blocking for efficient Markov chain Monte Carlo sampling (2017)
  12. Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
  13. Wang, Ying; Wong, Hoi Ying: VIX forecast under different volatility specifications (2017)
  14. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  15. Bakar, K. Shuvo; Kokic, Philip; Jin, Huidong: Hierarchical spatially varying coefficient and temporal dynamic process models using \textttspTDyn (2016)
  16. Chan, Jennifer S. K.: Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches (2016)
  17. Chan, Jennifer So Kuen; Wan, Wai Yin: Bayesian analysis of Cannabis offences using generalized Poisson geometric process model with flexible dispersion (2016)
  18. Chen, Hsiang-Chun; Wehrly, Thomas E.: Approximate uniform shrinkage prior for a multivariate generalized linear mixed model (2016)
  19. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  20. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)

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