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 218 articles )

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  1. Alvares, Danilo; Armero, Carmen; Forte, Anabel; Chopin, Nicolas: Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data (2021)
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  3. Kelter, Riko: Bayesian model selection in the (\mathcalM)-open setting -- approximate posterior inference and subsampling for efficient large-scale leave-one-out cross-validation via the difference estimator (2021)
  4. Kelter, Riko: Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality (2021)
  5. Ma, Zhihua; Chen, Guanghui: Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data (2021)
  6. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  7. Oh, Rosy; Lee, Youngju; Zhu, Dan; Ahn, Jae Youn: Predictive risk analysis using a collective risk model: choosing between past frequency and aggregate severity information (2021)
  8. Quintero, Adrian; Verbeke, Geert; Bruyneel, Luk; Lesaffre, Emmanuel: Bayesian analysis of differential effects in multi-group regression methods (2021)
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  11. Schramm, Pele; Batchelder, William H.: Hierarchical paired comparison modeling, a cultural consensus theory approach (2021)
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  15. Devecioğlu, İsmail; Güçlü, Burak: Psychophysical detection and learning in freely behaving rats: a probabilistic dynamical model for operant conditioning (2020)
  16. 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)
  17. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  18. Jobst, Lisa J.; Heck, Daniel W.; Moshagen, Morten: A comparison of correlation and regression approaches for multinomial processing tree models (2020)
  19. Lázaro, E.; Armero, C.; Gómez-Rubio, V.: Approximate Bayesian inference for mixture cure models (2020)
  20. 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)

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