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 699 articles , 2 standard articles )

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  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. Fouskakis, D.; Petrakos, G.; Rotous, I.: A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators (2020)
  4. Haining, Robert; Li, Guangquan: Modelling spatial and spatial-temporal data. A Bayesian approach (2020)
  5. Jouni Helske: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R (2020) arXiv
  6. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)
  7. Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari: BAT.jl - A Julia-based tool for Bayesian inference (2020) arXiv
  8. 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
  9. Soize, Christian; Ghanem, Roger G.; Desceliers, Christophe: Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset (2020)
  10. 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
  11. 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)
  12. Zhang, Hanze; Huang, Yangxin: Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study (2020)
  13. Ahmadi, Kambiz; Ghafouri, Somayeh: Reliability estimation in a multicomponent stress-strength model under generalized half-normal distribution based on progressive type-II censoring (2019)
  14. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  15. Ascari, Roberto; Migliorati, Sonia; Ongaro, Andrea: Bayesian inference for a mixture model on the simplex (2019)
  16. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Morales, Javier: Multivariate bioclimatic indices modelling: a coregionalised approach (2019)
  17. Broemeling, Lyle D.: Bayesian analysis of time series (2019)
  18. Corpas-Burgos, F.; Botella-Rocamora, P.; Martinez-Beneito, M. A.: On the convenience of heteroscedasticity in highly multivariate disease mapping (2019)
  19. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  20. Djeundje, Viani Biatat; Crook, Jonathan: Identifying hidden patterns in credit risk survival data using generalised additive models (2019)

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