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 251 articles )

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  1. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  2. 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)
  3. Carvalho, Rommel N.; Laskey, Kathryn B.; Costa, Paulo C.G.: PR-OWL - a language for defining probabilistic ontologies (2017)
  4. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  5. Houpt, Joseph W.; Fifić, Mario: A hierarchical Bayesian approach to distinguishing serial and parallel processing (2017)
  6. Ligtvoet, Rudy: Exact one-sided Bayes factors for 2 by 2 contingency tables (2017)
  7. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  8. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017)
  9. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  10. Turek, Daniel; de Valpine, Perry; Paciorek, Christopher J.; Anderson-Bergman, Clifford: Automated parameter blocking for efficient Markov chain Monte Carlo sampling (2017)
  11. Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
  12. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  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, Hsiang-Chun; Wehrly, Thomas E.: Approximate uniform shrinkage prior for a multivariate generalized linear mixed model (2016)
  15. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  16. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  17. Chiu, Chia-Yi; Köhn, Hans-Friedrich; Zheng, Yi; Henson, Robert: Joint maximum likelihood estimation for diagnostic classification models (2016)
  18. Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016)
  19. De Haan-Rietdijk, Silvia; Gottman, John M.; Bergeman, Cindy S.; Hamaker, Ellen L.: Get over it! A multilevel threshold autoregressive model for state-dependent affect regulation (2016)
  20. Dienes, Zoltan: How Bayes factors change scientific practice (2016)

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