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 173 articles )

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  1. Albert, Jim; Hu, Jingchen: Probability and Bayesian modeling (2020)
  2. Ferreira, Paulo H.; Ramos, Eduardo; Ramos, Pedro L.; Gonzales, Jhon F. B.; Tomazella, Vera L. D.; Ehlers, Ricardo S.; Silva, Eveliny B.; Louzada, Francisco: Objective Bayesian analysis for the Lomax distribution (2020)
  3. Lee, Michael D.; Bock, Jason R.; Cushman, Isaiah; Shankle, William R.: An application of multinomial processing tree models and Bayesian methods to understanding memory impairment (2020)
  4. Oh, Rosy; Shi, Peng; Ahn, Jae Youn: Bonus-malus premiums under the dependent frequency-severity modeling (2020)
  5. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  6. Amoros, Ruben; King, Ruth; Toyoda, Hidenori; Kumada, Takashi; Johnson, Philip J.; Bird, Thomas G.: A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma (2019)
  7. Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Quintero, Freddy O. López; Valdebenito, Abel: A skew-normal dynamic linear model and Bayesian forecasting (2019)
  8. Cox, Gregory E.; Criss, Amy H.: Parametric supplements to systems factorial analysis: identifying interactive parallel processing using systems of accumulators (2019)
  9. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  10. Finke, Axel; King, Ruth; Beskos, Alexandros; Dellaportas, Petros: Efficient sequential Monte Carlo algorithms for integrated population models (2019)
  11. Gelling, Nicholas; Schofield, Matthew R.; Barker, Richard J.: \textsfRpackage \textsfrjmcmc: reversible jump MCMC using post-processing (2019)
  12. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  13. Gronau, Quentin F.; Wagenmakers, Eric-Jan; Heck, Daniel W.; Matzke, Dora: A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling (2019)
  14. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  15. Heck, Daniel W.: Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018) (2019)
  16. Heck, Daniel W.; Davis-Stober, Clintin P.: Multinomial models with linear inequality constraints: overview and improvements of computational methods for Bayesian inference (2019)
  17. Heck, Daniel W.; Overstall, Antony M.; Gronau, Quentin F.; Wagenmakers, Eric-Jan: Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models (2019)
  18. Jona Lasinio, Giovanna; Pollice, Alessio; Fano, Elisa Anna: Generalized biodiversity assessment by Bayesian nested random effects models with spyke-and-slab priors (2019)
  19. Jure Demšar, Grega Repovš, Erik Štrumbelj: bayes4psy - an Open Source R Package for Bayesian Statistics in Psychology (2019) arXiv
  20. Kim, Gwangsu: Posterior consistency in frailty models and simulation studies to test the presence of random effects (2019)

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