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 608 articles , 2 standard articles )

Showing results 1 to 20 of 608.
Sorted by year (citations)

1 2 3 ... 29 30 31 next

  1. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  2. Haining, Robert; Li, Guangquan: Modelling spatial and spatial-temporal data. A Bayesian approach (2020)
  3. Zhang, Hanze; Huang, Yangxin: Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study (2020)
  4. Ahmadi, Kambiz; Ghafouri, Somayeh: Reliability estimation in a multicomponent stress-strength model under generalized half-normal distribution based on progressive type-II censoring (2019)
  5. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  6. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Morales, Javier: Multivariate bioclimatic indices modelling: a coregionalised approach (2019)
  7. Broemeling, Lyle D.: Bayesian analysis of time series (2019)
  8. Corpas-Burgos, F.; Botella-Rocamora, P.; Martinez-Beneito, M. A.: On the convenience of heteroscedasticity in highly multivariate disease mapping (2019)
  9. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  10. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)
  11. Franco, Glaura C.; Migon, Helio S.; Prates, Marcos O.: Time series of count data: a review, empirical comparisons and data analysis (2019)
  12. Gómez-Rubio, Virgilio; Palmí-Perales, Francisco; López-Abente, Gonzalo; Ramis-Prieto, Rebeca; Fernández-Navarro, Pablo: Bayesian joint spatio-temporal analysis of multiple diseases (2019)
  13. Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre: Bayesian functional linear regression with sparse step functions (2019)
  14. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  15. Hong, Maxwell R.; Jacobucci, Ross: Book review of: K. J. Grimm et al., Growth modeling. Structural equation and multilevel modeling approaches (2019)
  16. Jiang, Zhehan; Templin, Jonathan: Gibbs samplers for logistic item response models via the Pólya-gamma distribution: a computationally efficient data-augmentation strategy (2019)
  17. Johnson, Nels G.; Kim, Inyoung: Semiparametric approaches for matched case-control studies with error-in-covariates (2019)
  18. 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
  19. Karavarsamis, N.; Huggins, R. M.: Two-stage approaches to the analysis of occupancy data. II: The heterogeneous model and conditional likelihood (2019)
  20. Li, Kan; Luo, Sheng: Bayesian functional joint models for multivariate longitudinal and time-to-event data (2019)

1 2 3 ... 29 30 31 next