BUGS

The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. The project began in 1989 in the MRC Biostatistics Unit, Cambridge, and led initially to the `Classic’ BUGS program, and then onto the WinBUGS software developed jointly with the Imperial College School of Medicine at St Mary’s, London. Development is now focussed on the OpenBUGS project.


References in zbMATH (referenced in 378 articles )

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  1. Liu, Yang; Goudie, Robert J. B.: Stochastic approximation cut algorithm for inference in modularized Bayesian models (2022)
  2. Gunnarsson, Björn Rafn; vanden Broucke, Seppe; Baesens, Bart; Óskarsdóttir, María; Lemahieu, Wilfried: Deep learning for credit scoring: do or don’t? (2021)
  3. Hu, Jinxiang; Clark, Lauren; Shi, Peng; Staggs, Vincent S.; Daley, Christine; Gajewski, Byron: Bayesian hierarchical factor analysis for eficient estimation across race/ethnicity (2021)
  4. Kazemi, Iraj; Hassanzadeh, Fatemeh: Marginalized random-effects models for clustered binomial data through innovative link functions (2021)
  5. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  6. Perry de Valpine, Sally Paganin, Daniel Turek: compareMCMCs: An R package for studying MCMC efficiency (2021) not zbMATH
  7. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  8. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  9. Shang, Han Lin: Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density (2021)
  10. Wang, Zhenxun; Lin, Lifeng; Murray, Thomas; Hodges, James S.; Chu, Haitao: Bridging randomized controlled trials and single-arm trials using commensurate priors in arm-based network meta-analysis (2021)
  11. de Zea Bermudez, P.; Marín, J. Miguel; Veiga, Helena: Data cloning estimation for asymmetric stochastic volatility models (2020)
  12. Farzammehr, Mohadeseh Alsadat; Zadkarami, Mohammad Reza; McLachlan, Geoffrey J.; Lee, Sharon X.: Skew-normal Bayesian spatial heterogeneity panel data models (2020)
  13. Fuglstad, Geir-Arne; Hem, Ingeborg Gullikstad; Knight, Alexander; Rue, Håvard; Riebler, Andrea: Intuitive joint priors for variance parameters (2020)
  14. Kazemi, Iraj; Hassanzadeh, Fatemeh: Modelling multivariate, overdispersed count data with correlated and non-normal heterogeneity effects (2020)
  15. Li, Yong; Yu, Jun; Zeng, Tao: Deviance information criterion for latent variable models and misspecified models (2020)
  16. 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
  17. 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
  18. Zheng, Y. X.; Zhang, Y. H.; Lu, X. H.: On the volatility of high frequency stock index based on SV model of MCMC (2020)
  19. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  20. Baer, Daniel R.; Lawson, Andrew B.: Evaluation of Bayesian multiple stage estimation under spatial CAR model variants (2019)

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