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 209 articles )

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  1. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  2. 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)
  3. Ryan Hornby, Jingchen Hu: Bayesian Estimation of Attribute Disclosure Risks in Synthetic Data with the AttributeRiskCalculation R Package (2021) arXiv
  4. Albert, Jim; Hu, Jingchen: Probability and Bayesian modeling (2020)
  5. Anne Philippe, Marie-Anne Vibet: Analysis of Archaeological Phases Using the R Package ArchaeoPhases (2020) not zbMATH
  6. de Castro, Mário; Gómez, Yolanda M.: A Bayesian cure rate model based on the power piecewise exponential distribution (2020)
  7. 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)
  8. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  9. Jobst, Lisa J.; Heck, Daniel W.; Moshagen, Morten: A comparison of correlation and regression approaches for multinomial processing tree models (2020)
  10. Lázaro, E.; Armero, C.; Gómez-Rubio, V.: Approximate Bayesian inference for mixture cure models (2020)
  11. 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)
  12. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)
  13. Merkle, Edgar C.; Saw, Geoff; Davis-Stober, Clintin: Beating the average forecast: regularization based on forecaster attributes (2020)
  14. Michalkiewicz, Martha; Horn, Sebastian S.; Bayen, Ute J.: Hierarchical multinomial modeling to explain individual differences in children’s clustering in free recall (2020)
  15. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  16. Oh, Rosy; Shi, Peng; Ahn, Jae Youn: Bonus-malus premiums under the dependent frequency-severity modeling (2020)
  17. Oravecz, Zita; Vandekerckhove, Joachim: A joint process model of consensus and longitudinal dynamics (2020)
  18. Osthus, Dave; Hyman, Jeffrey D.; Karra, Satish; Panda, Nishant; Srinivasan, Gowri: A probabilistic clustering approach for identifying primary subnetworks of discrete fracture networks with quantified uncertainty (2020)
  19. Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis, Andrew Thomas: MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference (2020) not zbMATH
  20. Stojić, Hrvoje; Orquin, Jacob L.; Dayan, Peter; Dolan, Raymond J.; Speekenbrink, Maarten: Uncertainty in learning, choice, and visual fixation (2020)

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