WinBUGS

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 776 articles , 2 standard articles )

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  1. Ariyo, Oludare; Lesaffre, Emmanuel; Verbeke, Geert; Quintero, Adrian: Model selection for Bayesian linear mixed models with longitudinal data: sensitivity to the choice of priors (2022)
  2. Caliendo, Ciro; Guida, Maurizio; Postiglione, Fabio; Russo, Isidoro: A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels (2022)
  3. Hamura, Yasuyuki; Irie, Kaoru; Sugasawa, Shonosuke: Log-regularly varying scale mixture of normals for robust regression (2022)
  4. He, Yulei; Zhang, Guangyu; Hsu, Chiu-Hsieh: Multiple imputation of missing data in practice. Basic theory and analysis strategies (2022)
  5. Jreich, Rana; Hatte, Christine; Parent, Eric: Review of Bayesian selection methods for categorical predictors using JAGS (2022)
  6. Marsman, M.; Huth, K.; Waldorp, L. J.; Ntzoufras, I.: Objective Bayesian edge screening and structure selection for Ising networks (2022)
  7. Martins, Rui: A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity (2022)
  8. Mohammadi, Raziyeh; Kazemi, Iraj: A robust linear mixed-effects model for longitudinal data using an innovative multivariate skew-Huber distribution (2022)
  9. Wan, Zhonglin; Li, Hongyan; Luo, Yi; Huang, Yirong: A novel Bayesian approach to estimate long memory parameter (2022)
  10. Yamaguchi, Kazuhiro; Templin, Jonathan: A Gibbs sampling algorithm with monotonicity constraints for diagnostic classification models (2022)
  11. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  12. Calvo, Gabriel; Armero, Carmen; Gómez-Rubio, Virgilio; Mazzinari, Guido: Bayesian hierarchical nonlinear modelling of intra-abdominal volume during pneumoperitoneum for laparoscopic surgery (2021)
  13. Chen, Li-Chieh; Su, Jianxi; Xia, Michelle: Two-part models for assessing misrepresentation on risk status (2021)
  14. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  15. Gardini, Aldo; Trivisano, Carlo; Fabrizi, Enrico: Bayesian analysis of ANOVA and mixed models on the log-transformed response variable (2021)
  16. Hartmann, Raphael; Klauer, Karl Christoph: Partial derivatives for the first-passage time distribution in Wiener diffusion models (2021)
  17. Hu, Jinxiang; Clark, Lauren; Shi, Peng; Staggs, Vincent S.; Daley, Christine; Gajewski, Byron: Bayesian hierarchical factor analysis for eficient estimation across race/ethnicity (2021)
  18. Karling, M. J.; Lopes, S. R. C.; de Souza, R. M.: A Bayesian approach for estimating the parameters of an (\alpha)-stable distribution (2021)
  19. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  20. Martins, Rui; Caldeira, Jorge; Lopes, Inês; João Mendes, José: Improving teeth aesthetics using a spatially shared-parameters model for independent regular lattices (2021)

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