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. Carlin, John B.; Wolfe, Rory; Brown, C. Hendricks; Gelman, Andrew: A case study on the choice, interpretation and checking of multilevel models for longitudinal binary outcomes (2001)
  2. Chen, Ming-Hui; Ibrahim, Joseph G.: Maximum likelihood methods for cure rate models with missing covariates (2001)
  3. Congdon, Peter: Predicting adverse infant health outcomes using routine screening variables: modelling the impact of interdependent risk factors. (2001)
  4. Dunson, David B.; Perreault, Sally D.: Factor analytic models of clustered multivariate data with informative censoring (2001)
  5. Everson, Philip J.: Exact Bayesian inference for normal hierarchical models (2001)
  6. Gelfand, Alan E.; Carlin, Bradley P.; Trevisiani, Matilde: On computation using Gibbs sampling for multilevel models (2001)
  7. Gill, Paramjit S.; Swartz, Tim B.: Statistical analyses for round robin interaction data (2001)
  8. Gran, Bjørn Axel; Helminen, Atte: A Bayesian belief network for reliability assessment (2001)
  9. Ibrahim, Joseph G.; Chen, Ming-Hui; Sinha, Debajyoti: Bayesian survival analysis (2001)
  10. McDonald, John W.; Rosina, Alessandro: Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals (2001)
  11. Natarajan, Ranjini: On the propriety of a modified Jeffreys’s prior for variance components in binary random effects models. (2001)
  12. Scollnik, David P. M.: Actuarial modeling with MCMC and BUGS. With a discussion by David Spiegelhalter. (2001)
  13. Barnard, John: MiPy: a system for generating multiple imputations (2000)
  14. Best, Nicola G.; Ickstadt, Katja; Wolpert, Robert L.: Spatial Poisson regression for health and exposure data measured at disparate resolutions (2000)
  15. Branco, M.; Bolfarine, H.; Iglesias, P.; Arellano-Valle, R. B.: Bayesian analysis of the calibration problem under elliptical distributions. (2000)
  16. Carlin, Bradley P.; Louis, Thomas A.: Empirical Bayes: Past, present and future. (2000)
  17. Carlin, Bradley P.; Pérez, María-Eglée: Robust Bayesian analysis in medical and epidemiological settings (2000)
  18. Congdon, Peter; Best, Nicky: Small area variation in hospital admission rates: Bayesian adjustment for primary care and hospital factors (2000)
  19. Dellaportas, Petros; Forster, Jonathan J.; Ntzoufras, Ioannis: Bayesian variable selection using the Gibbs sampler (2000)
  20. Diggle, Peter J.; Morris, Sara E.; Wakefield, Jon C.: Point-source modelling using matched case-control data (2000)

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