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 41 articles )

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  1. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  2. Dalla Valle, Luciana; De Giuli, Maria Elena; Tarantola, Claudia; Manelli, Claudio: Default probability estimation via pair copula constructions (2016)
  3. Fullerton, Andrew S.; Xu, Jun: Ordered regression models. Parallel, partial, and non-parallel alternatives (2016)
  4. Heck, Daniel W.; Wagenmakers, Eric-Jan: Adjusted priors for Bayes factors involving reparameterized order constraints (2016)
  5. Hilbe, Joseph M.: Practical guide to logistic regression (2016)
  6. Irvine, Kathryn M.; Rodhouse, T.J.; Keren, Ilai N.: Extending ordinal regression with a latent zero-augmented beta distribution (2016)
  7. Kary, Arthur; Taylor, Robert; Donkin, Chris: Using Bayes factors to test the predictions of models: a case study in visual working memory (2016)
  8. Katahira, Kentaro: How hierarchical models improve point estimates of model parameters at the individual level (2016)
  9. 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)
  10. Lim, Kar Wai; Buntine, Wray; Chen, Changyou; Du, Lan: Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes (2016)
  11. Okada, Kensuke; Lee, Michael D.: A Bayesian approach to modeling group and individual differences in multidimensional scaling (2016)
  12. Ruli, Erlis; Sartori, Nicola; Ventura, Laura: Improved Laplace approximation for marginal likelihoods (2016)
  13. Shiffrin, Richard M.; Chandramouli, Suyog H.; Grünwald, Peter D.: Bayes factors, relations to minimum description length, and overlapping model classes (2016)
  14. 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
  15. Anders, Royce; Batchelder, William H.: Cultural consensus theory for the ordinal data case (2015)
  16. Ferkingstad, Egil; Rue, Håvard: Improving the INLA approach for approximate Bayesian inference for latent Gaussian models (2015)
  17. Jabot, Franck: Why preferring parametric forecasting to nonparametric methods? (2015)
  18. Matzke, Dora; Dolan, Conor V.; Batchelder, William H.; Wagenmakers, Eric-Jan: Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items (2015)
  19. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  20. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)

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