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 173 articles )

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  1. Chen, Hsiang-Chun; Wehrly, Thomas E.: Approximate uniform shrinkage prior for a multivariate generalized linear mixed model (2016)
  2. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  3. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  4. 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)
  5. Dienes, Zoltan: How Bayes factors change scientific practice (2016)
  6. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  7. Shang, Han Lin: A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data (2016)
  8. Anders, Royce; Batchelder, William H.: Cultural consensus theory for the ordinal data case (2015)
  9. Fried, Roland; Agueusop, Inoncent; Bornkamp, Björn; Fokianos, Konstantinos; Fruth, Jana; Ickstadt, Katja: Retrospective Bayesian outlier detection in INGARCH series (2015)
  10. Fu, Shuai: A hierarchical Bayesian approach to negative binomial regression (2015)
  11. Gill, Jeff: Bayesian methods. A social and behavioral sciences approach (2015)
  12. Liu, Ying; Gelman, Andrew; Zheng, Tian: Simulation-efficient shortest probability intervals (2015)
  13. Matzke, Dora; Dolan, Conor V.; Batchelder, William H.; Wagenmakers, Eric-Jan: Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items (2015)
  14. Mostafa, Ayman A.: Bayesian analysis technique for generalized Cox’s proportional hazards model using BUGS: applications in medical data (2015)
  15. Oravecz, Zita; Anders, Royce; Batchelder, William H.: Hierarchical Bayesian modeling for test theory without an answer key (2015)
  16. Plummer, Martyn: Cuts in Bayesian graphical models (2015)
  17. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  18. Bartlema, Annelies; Lee, Michael; Wetzels, Ruud; Vanpaemel, Wolf: A Bayesian hierarchical mixture approach to individual differences: case studies in selective attention and representation in category learning (2014)
  19. Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; Vehtari, Akti; Rubin, Donald B.: Bayesian data analysis. (2014)
  20. Gelman, Andrew; Hwang, Jessica; Vehtari, Aki: Understanding predictive information criteria for Bayesian models (2014)

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