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 116 articles )

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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Betz, Jennifer; Kellner, Ralf; Rösch, Daniel: Systematic effects among loss given defaults and their implications on downturn estimation (2018)
  3. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  4. Depaoli, Sarah; Liu, Yang: Book review of: R. Levy and R. J. Mislevy, Bayesian psychometric modeling (2018)
  5. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  6. Higueras, Manuel; Howes, Adam: Poisson excess relative risk models: new implementations and software (2018)
  7. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  8. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  9. Klauer, Karl Christoph; Kellen, David: RT-MPTs: process models for response-time distributions based on multinomial processing trees with applications to recognition memory (2018)
  10. Mair, Patrick: Modern psychometrics with R (2018)
  11. Ma, Zhihua; Chen, Guanghui: Bayesian methods for dealing with missing data problems (2018)
  12. Molenaar, Dylan; de Boeck, Paul: Response mixture modeling: accounting for heterogeneity in item characteristics across response times (2018)
  13. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  14. Palestro, James J.; Bahg, Giwon; Sederberg, Per B.; Lu, Zhong-Lin; Steyvers, Mark; Turner, Brandon M.: A tutorial on joint models of neural and behavioral measures of cognition (2018)
  15. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  16. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  17. Benavoli, Alessio; Corani, Giorgio; Demšar, Janez; Zaffalon, Marco: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis (2017)
  18. Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017) not zbMATH
  19. Boca, Simina M.; Pfeiffer, Ruth M.; Sampson, Joshua N.: Multivariate meta-analysis with an increasing number of parameters (2017)
  20. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017) not zbMATH

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