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 674 articles , 2 standard articles )

Showing results 1 to 20 of 674.
Sorted by year (citations)

1 2 3 ... 32 33 34 next

  1. Castillo-Carreno, Edwin; Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente: Bayesian structured antedependence model proposals for longitudinal data (2020)
  2. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  3. Haining, Robert; Li, Guangquan: Modelling spatial and spatial-temporal data. A Bayesian approach (2020)
  4. Jouni Helske: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R (2020) arXiv
  5. Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis, Andrew Thomas: MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference (2020) not zbMATH
  6. Timothy D. Meehan, Nicole L. Michel, Håvard Rue: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R (2020) not zbMATH
  7. Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F.: Properties of the bridge sampler with a focus on splitting the MCMC sample (2020)
  8. Zhang, Hanze; Huang, Yangxin: Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study (2020)
  9. Ahmadi, Kambiz; Ghafouri, Somayeh: Reliability estimation in a multicomponent stress-strength model under generalized half-normal distribution based on progressive type-II censoring (2019)
  10. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  11. Ascari, Roberto; Migliorati, Sonia; Ongaro, Andrea: Bayesian inference for a mixture model on the simplex (2019)
  12. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Morales, Javier: Multivariate bioclimatic indices modelling: a coregionalised approach (2019)
  13. Broemeling, Lyle D.: Bayesian analysis of time series (2019)
  14. Corpas-Burgos, F.; Botella-Rocamora, P.; Martinez-Beneito, M. A.: On the convenience of heteroscedasticity in highly multivariate disease mapping (2019)
  15. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  16. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)
  17. Franco, Glaura C.; Migon, Helio S.; Prates, Marcos O.: Time series of count data: a review, empirical comparisons and data analysis (2019)
  18. Gómez-Rubio, Virgilio; Palmí-Perales, Francisco; López-Abente, Gonzalo; Ramis-Prieto, Rebeca; Fernández-Navarro, Pablo: Bayesian joint spatio-temporal analysis of multiple diseases (2019)
  19. Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre: Bayesian functional linear regression with sparse step functions (2019)
  20. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv

1 2 3 ... 32 33 34 next