JAGS

JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: (1) To have a cross-platform engine for the BUGS language. (2) To be extensible, allowing users to write their own functions, distributions and samplers. (3) To be a plaftorm for experimentation with ideas in Bayesian modelling. JAGS is licensed under the GNU General Public License. You may freely modify and redistribute it under certain conditions (see the file COPYING for details).


References in zbMATH (referenced in 50 articles )

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  1. Boca, Simina M.; Pfeiffer, Ruth M.; Sampson, Joshua N.: Multivariate meta-analysis with an increasing number of parameters (2017)
  2. Coley, Rebecca Yates; Fisher, Aaron J.; Mamawala, Mufaddal; Carter, Herbert Ballentine; Pienta, Kenneth J.; Zeger, Scott L.: A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer (2017)
  3. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  4. Lahoz-Monfort, José J.; Harris, Michael P.; Wanless, Sarah; Freeman, Stephen N.; Morgan, Byron J.T.: Bringing it all together: multi-species integrated population modelling of a breeding community (2017)
  5. Lanzarone, E.; Pasquali, S.; Gilioli, G.; Marchesini, E.: A Bayesian estimation approach for the mortality in a stage-structured demographic model (2017)
  6. Nunez, Michael D.; Vandekerckhove, Joachim; Srinivasan, Ramesh: How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters (2017)
  7. Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv
  8. Dalla Valle, Luciana; De Giuli, Maria Elena; Tarantola, Claudia; Manelli, Claudio: Default probability estimation via pair copula constructions (2016)
  9. Fullerton, Andrew S.; Xu, Jun: Ordered regression models. Parallel, partial, and non-parallel alternatives (2016)
  10. Heck, Daniel W.; Wagenmakers, Eric-Jan: Adjusted priors for Bayes factors involving reparameterized order constraints (2016)
  11. Hilbe, Joseph M.: Practical guide to logistic regression (2016)
  12. Irvine, Kathryn M.; Rodhouse, T.J.; Keren, Ilai N.: Extending ordinal regression with a latent zero-augmented beta distribution (2016)
  13. Kary, Arthur; Taylor, Robert; Donkin, Chris: Using Bayes factors to test the predictions of models: a case study in visual working memory (2016)
  14. Katahira, Kentaro: How hierarchical models improve point estimates of model parameters at the individual level (2016)
  15. Li, Longhai; Qiu, Shi; Zhang, Bei; Feng, Cindy X.: Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC (2016)
  16. Lim, Kar Wai; Buntine, Wray; Chen, Changyou; Du, Lan: Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes (2016)
  17. Okada, Kensuke; Lee, Michael D.: A Bayesian approach to modeling group and individual differences in multidimensional scaling (2016)
  18. Ruli, Erlis; Sartori, Nicola; Ventura, Laura: Improved Laplace approximation for marginal likelihoods (2016)
  19. Shiffrin, Richard M.; Chandramouli, Suyog H.; Grünwald, Peter D.: Bayes factors, relations to minimum description length, and overlapping model classes (2016)
  20. Victoria N Nyaga, Marc Arbyn, Marc Aerts: CopulaDTA: An R Package for Copula Based Bivariate Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework (2016) arXiv

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