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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  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)
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  20. Kim, Gwangsu: Posterior consistency in frailty models and simulation studies to test the presence of random effects (2019)

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