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 261 articles )

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  1. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018)
  2. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018)
  3. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018)
  4. Ma, Zhihua; Chen, Guanghui: Bayesian methods for dealing with missing data problems (2018)
  5. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  6. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  7. 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)
  8. Carvalho, Rommel N.; Laskey, Kathryn B.; Costa, Paulo C. G.: PR-OWL - a language for defining probabilistic ontologies (2017)
  9. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  10. Houpt, Joseph W.; Fifić, Mario: A hierarchical Bayesian approach to distinguishing serial and parallel processing (2017)
  11. Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.: Automatic differentiation variational inference (2017)
  12. Ligtvoet, Rudy: Exact one-sided Bayes factors for 2 by 2 contingency tables (2017)
  13. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  14. 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)
  15. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017)
  16. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  17. Turek, Daniel; de Valpine, Perry; Paciorek, Christopher J.; Anderson-Bergman, Clifford: Automated parameter blocking for efficient Markov chain Monte Carlo sampling (2017)
  18. Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)
  19. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  20. Chan, Jennifer S. K.: Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches (2016)

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