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 591 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. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Morales, Javier: Multivariate bioclimatic indices modelling: a coregionalised approach (2019)
  3. Broemeling, Lyle D.: Bayesian analysis of time series (2019)
  4. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  5. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)
  6. Franco, Glaura C.; Migon, Helio S.; Prates, Marcos O.: Time series of count data: a review, empirical comparisons and data analysis (2019)
  7. Gómez-Rubio, Virgilio; Palmí-Perales, Francisco; López-Abente, Gonzalo; Ramis-Prieto, Rebeca; Fernández-Navarro, Pablo: Bayesian joint spatio-temporal analysis of multiple diseases (2019)
  8. Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre: Bayesian functional linear regression with sparse step functions (2019)
  9. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  10. Hong, Maxwell R.; Jacobucci, Ross: Book review: K. J. Grimm et al., Review of growth modeling: structural equation and multilevel modeling approaches. (2019)
  11. Jiang, Zhehan; Templin, Jonathan: Gibbs samplers for logistic item response models via the Pólya-gamma distribution: a computationally efficient data-augmentation strategy (2019)
  12. Johnson, Nels G.; Kim, Inyoung: Semiparametric approaches for matched case-control studies with error-in-covariates (2019)
  13. 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
  14. Karavarsamis, N.; Huggins, R. M.: Two-stage approaches to the analysis of occupancy data. II: The heterogeneous model and conditional likelihood (2019)
  15. Li, Kan; Luo, Sheng: Bayesian functional joint models for multivariate longitudinal and time-to-event data (2019)
  16. Mahdiyeh, Zahra; Kazemi, Iraj: An innovative strategy on the construction of multivariate multimodal linear mixed-effects models (2019)
  17. Merkle, Edgar C.; Furr, Daniel; Rabe-Hesketh, Sophia: Bayesian comparison of latent variable models: conditional versus marginal likelihoods (2019)
  18. Prates, Marcos Oliveira; Assunção, Renato Martins; Rodrigues, Erica Castilho: Alleviating spatial confounding for areal data problems by displacing the geographical centroids (2019)
  19. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  20. Yamrubboon, Darika; Thongteeraparp, Ampai; Bodhisuwan, Winai; Jampachaisri, Katechan; Volodin, Andrei: Bayesian inference for the negative binomial-Sushila linear model (2019)

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