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 299 articles )

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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)
  3. Ng, Kenyon; Turlach, Berwin A.; Murray, Kevin: A flexible sequential Monte Carlo algorithm for parametric constrained regression (2019)
  4. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  5. Wiśniowski, Arkadiusz; Bijak, Jakub; Forster, Jonathan J.; Smith, Peter W. F.: Hierarchical model for forecasting the outcomes of binary referenda (2019)
  6. Cozman, Fabio G.; Mauá, Denis D.: The complexity of Bayesian networks specified by propositional and relational languages (2018)
  7. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  8. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  9. Hassan, Andrés Ramírez; Pericchi, Luis: Effects of prior distributions: an application to piped water demand (2018)
  10. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  11. Lock, Eric F.; Kohli, Nidhi; Bose, Maitreyee: Detecting multiple random changepoints in Bayesian piecewise growth mixture models (2018)
  12. Ma, Zhihua; Chen, Guanghui: Bayesian methods for dealing with missing data problems (2018)
  13. Migliorati, Sonia; Di Brisco, Agnese Maria; Ongaro, Andrea: A new regression model for bounded responses (2018)
  14. Molenaar, Dylan; de Boeck, Paul: Response mixture modeling: accounting for heterogeneity in item characteristics across response times (2018)
  15. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  16. Sun, BaoLuo; Tchetgen Tchetgen, Eric J.: On inverse probability weighting for nonmonotone missing at random data (2018)
  17. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  18. Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F.: Bayesian mortality forecasting with overdispersion (2018)
  19. Achcar, Jorge Alberto; Molina de Souza, Roberto; Coelho-Barros, Emílio Augusto: Stochastic volatility models (SVM) in the analysis of drought periods (2017)
  20. Baio, Gianluca; Berardi, Andrea; Heath, Anna: Bayesian cost-effectiveness analysis with the R package BCEA (2017)

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