WinBUGS is part of the BUGS project, which aims to make practical MCMC methods available to applied statisticians. WinBUGS can use either a standard ’point-and-click’ windows interface for controlling the analysis, or can construct the model using a graphical interface called DoodleBUGS. WinBUGS is a stand-alone program, although it can be called from other software.

References in zbMATH (referenced in 555 articles , 2 standard articles )

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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  3. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)
  4. Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre: Bayesian functional linear regression with sparse step functions (2019)
  5. Jonathon Love; Ravi Selker; Maarten Marsman; Tahira Jamil; Damian Dropmann; Josine Verhagen; Alexander Ly; Quentin Gronau; Martin Šmíra; Sacha Epskamp; Dora Matzke; Anneliese Wild; Patrick Knight; Jeffrey Rouder; Richard Morey; Eric-Jan Wagenmakers: JASP: Graphical Statistical Software for Common Statistical Designs (2019) not zbMATH
  6. Karavarsamis, N.; Huggins, R. M.: Two-stage approaches to the analysis of occupancy data. II: The heterogeneous model and conditional likelihood (2019)
  7. Li, Kan; Luo, Sheng: Bayesian functional joint models for multivariate longitudinal and time-to-event data (2019)
  8. Broemeling, Lyle D.: Bayesian inference for stochastic processes (2018)
  9. Consonni, Guido; Fouskakis, Dimitris; Liseo, Brunero; Ntzoufras, Ioannis: Prior distributions for objective Bayesian analysis (2018)
  10. Dhavale, Dileep G.; Sarkis, Joseph: Stochastic internal rate of return on investments in sustainable assets generating carbon credits (2018)
  11. Dobson, Annette J.; Barnett, Adrian G.: An introduction to generalized linear models (2018)
  12. Duan, Fengjun; Wang, Guanjun; Wang, Huan: Inverse Gaussian process models for bivariate degradation analysis: a Bayesian perspective (2018)
  13. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  14. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  15. Giordano, Ryan; Broderick, Tamara; Jordan, Michael I.: Covariances, robustness, and variational Bayes (2018)
  16. Jan Luts; Shen Wang; John Ormerod; Matt Wand: Semiparametric Regression Analysis via Infer.NET (2018) not zbMATH
  17. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  18. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  19. Kim, Hea-Jung: Bayesian hierarchical robust factor analysis models for partially observed sample-selection data (2018)
  20. Lindqvist, Bo H.; Taraldsen, Gunnar: On the proper treatment of improper distributions (2018)

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