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 516 articles , 2 standard articles )

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  1. Li, Kan; Luo, Sheng: Bayesian functional joint models for multivariate longitudinal and time-to-event data (2019)
  2. Broemeling, Lyle D.: Bayesian inference for stochastic processes (2018)
  3. Dhavale, Dileep G.; Sarkis, Joseph: Stochastic internal rate of return on investments in sustainable assets generating carbon credits (2018)
  4. Duan, Fengjun; Wang, Guanjun; Wang, Huan: Inverse Gaussian process models for bivariate degradation analysis: a Bayesian perspective (2018)
  5. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018)
  6. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  7. Jan Luts; Shen Wang; John Ormerod; Matt Wand: Semiparametric Regression Analysis via Infer.NET (2018)
  8. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018)
  9. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018)
  10. Kim, Hea-Jung: Bayesian hierarchical robust factor analysis models for partially observed sample-selection data (2018)
  11. Lindqvist, Bo H.; Taraldsen, Gunnar: On the proper treatment of improper distributions (2018)
  12. Morrison, Rebecca E.; Oliver, Todd A.; Moser, Robert D.: Representing model inadequacy: a stochastic operator approach (2018)
  13. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  14. Pinto Junior, Jony Arrais; Da Silva, Patrícia Viana: Bayesian variable selection methods for log-Gaussian Cox processes (2018)
  15. Romeo, Jose S.; Meyer, Renate; Gallardo, Diego I.: Bayesian bivariate survival analysis using the power variance function copula (2018)
  16. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018)
  17. Alvares, Danilo; Armero, Carmen; Forte, Anabel; Chopin, Nicolas: Sequential Monte Carlo methods in random intercept models for longitudinal data (2017)
  18. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Mayoral, Asunción; Morales, Javier; Barber, Antoni: Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices (2017)
  19. Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017)
  20. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017)

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