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 739 articles , 2 standard articles )

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  1. He, Yulei; Zhang, Guangyu; Hsu, Chiu-Hsieh: Multiple imputation of missing data in practice. Basic theory and analysis strategies (2022)
  2. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  3. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  4. Hartmann, Raphael; Klauer, Karl Christoph: Partial derivatives for the first-passage time distribution in Wiener diffusion models (2021)
  5. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  6. Mayrink, V. D., Duarte, J. D. N., Demarqui, F. N.: pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution (2021) not zbMATH
  7. Ma, Zhihua; Chen, Guanghui: Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data (2021)
  8. Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., Nguyen, D.: Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages (2021) not zbMATH
  9. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  10. Okhli, Kheirolah; Jabbari Nooghabi, Mehdi: On the contaminated exponential distribution: a theoretical Bayesian approach for modeling positive-valued insurance claim data with outliers (2021)
  11. Prakash, Atma; Hazra, Budhaditya; Sreedeep, S.: Probabilistic analysis of soil-water characteristic curve using limited data (2021)
  12. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  13. Skevas, Ioannis; Skevas, Theodoros: A generalized true random-effects model with spatially autocorrelated persistent and transient inefficiency (2021)
  14. Umlauf, N., Klein, N., Simon, T., Zeileis, A: bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond) (2021) not zbMATH
  15. Castillo-Carreno, Edwin; Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente: Bayesian structured antedependence model proposals for longitudinal data (2020)
  16. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  17. Fouskakis, D.; Petrakos, G.; Rotous, I.: A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators (2020)
  18. Haining, Robert; Li, Guangquan: Modelling spatial and spatial-temporal data. A Bayesian approach (2020)
  19. Jouni Helske: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R (2020) arXiv
  20. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)

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