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 364 articles )

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  1. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  2. Li, Yong; Yu, Jun; Zeng, Tao: Deviance information criterion for latent variable models and misspecified models (2020)
  3. 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
  4. 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
  5. Zheng, Y. X.; Zhang, Y. H.; Lu, X. H.: On the volatility of high frequency stock index based on SV model of MCMC (2020)
  6. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  7. Baer, Daniel R.; Lawson, Andrew B.: Evaluation of Bayesian multiple stage estimation under spatial CAR model variants (2019)
  8. Cozman, Fabio Gagliardi; Mauá, Denis Deratani: The finite model theory of Bayesian network specifications: descriptive complexity and zero/one laws (2019)
  9. Dryden, Ian L.; Kim, Kwang-Rae; Le, Huiling: Bayesian linear size-and-shape regression with applications to face data (2019)
  10. Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH
  11. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)
  12. Lehnert, Judith; Kolbitsch, Christoph; Wübbeler, Gerd; Chiribiri, Amedeo; Schaeffter, Tobias; Elster, Clemens: Large-scale Bayesian spatial-temporal regression with application to cardiac MR-perfusion imaging (2019)
  13. Ng, Kenyon; Turlach, Berwin A.; Murray, Kevin: A flexible sequential Monte Carlo algorithm for parametric constrained regression (2019)
  14. Peng, Weiwen; Zhu, Shun-Peng; Shen, Lijuan: The transformed inverse Gaussian process as an age- and state-dependent degradation model (2019)
  15. Qian, Yanjun; Huang, Jianhua Z.; Park, Chiwoo; Ding, Yu: Fast dynamic nonparametric distribution tracking in electron microscopic data (2019)
  16. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  17. Wiśniowski, Arkadiusz; Bijak, Jakub; Forster, Jonathan J.; Smith, Peter W. F.: Hierarchical model for forecasting the outcomes of binary referenda (2019)
  18. Xu, X.; Lu, P.; MacEachern, S. N.; Xu, R.: Calibrated Bayes factors for model comparison (2019)
  19. Bou-Rabee, Nawaf; Sanz-Serna, J. M.: Geometric integrators and the Hamiltonian Monte Carlo method (2018)
  20. Cozman, Fabio G.; Mauá, Denis D.: The complexity of Bayesian networks specified by propositional and relational languages (2018)

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