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. Gehan, Edmund A.: Biostatistics in the new millennium: a consulting statistician’s perspective (2000)
  2. Meyer, Renate; Yu, Jun: BUGS for a Bayesian analysis of stochastic volatility models (2000)
  3. Natarajan, Ranjini; Kass, Robert E.: Reference Bayesian methods for generalized linear mixed models (2000)
  4. van der Linde, Angelika: Reference priors for shrinkage and smoothing parameters (2000)
  5. Wakefield, Jonathan; Rahman, Nargis: The combination of population pharmacokinetic studies (2000)
  6. Zheng, Beiyao: Bayesian estimation of multidimensional item response theory model using Gibbs sampling (2000)
  7. Cowles, Mary Kathryn; Roberts, Gareth O.; Rosenthal, Jeffrey S.: Possible biases induced by MCMC convergence diagnostics. (1999)
  8. George, A. W.; Mengersen, K. L.; Davis, G. P.: A Bayesian approach to ordering gene markers (1999)
  9. Ghosh, Malay; Natarajan, Kannan; Waller, Lance A.; Kim, Dalho: Hierarchical Bayes GLMs for the analysis of spatial data: An application to disease mapping (1999)
  10. Hughes, James P.: Mixed effects models with censored data with application to HIV RNA levels (1999)
  11. Ickstadt, Katja; Wolpert, Robert L.: Spatial regression for marked point processes. (With discussion) (1999)
  12. Langford, Ian H.; Leyland, Alastair H.; Rasbash, Jon; Goldstein, Harvey: Multilevel modelling of the geographical distributions of diseases (1999)
  13. Liao, J. G.: A hierarchical Bayesian model for combining multiple (2\times2) tables using conditional likelihoods (1999)
  14. Ten Have, Thomas R.; Localio, A. Russell: Empirical Bayes estimation of random effects parameters in mixed effects logistic regression models (1999)
  15. Toivonen, Hannu T. T.; Mannila, Heikki; Salmenkivi, Marko; Laakso, Karri-Pekka: Specifying and simulating complex models using Bassist (1999)
  16. Stangl, Dalene K.; Berry, Donald A.: Bayesian statistics in medicine: Where are we and where should we be going? (1998)
  17. McDonald, J. W.; Prevost, A. T.: The fitting of parameter-constrained demographic models (1997)
  18. Sahu, Sujit K.; Dey, Dipak K.; Aslanidou, Helen; Sinha, Debajyoti: A Weibull regression model with gamma frailties for multivariate survival data (1997)
  19. Athreya, Krishna B.; Doss, Hani; Sethuraman, Jayaram: On the convergence of the Markov chain simulation method (1996)
  20. Casella, George: Statistical inference and Monte Carlo algorithms. (With discussion) (1996)

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