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Just Another Gibbs Sampler (JAGS)

Author

Listed:
  • Sarah Depaoli
  • James P. Clifton
  • Patrice R. Cobb

    (University of California, Merced)

Abstract

A review of the software Just Another Gibbs Sampler (JAGS) is provided. We cover aspects related to history and development and the elements a user needs to know to get started with the program, including (a) definition of the data, (b) definition of the model, (c) compilation of the model, and (d) initialization of the model. An example using a latent class model with large-scale education data is provided to illustrate how easily JAGS can be implemented in R. We also cover details surrounding the many programs implementing JAGS. We conclude with a discussion of the newest features and upcoming developments. JAGS is constantly evolving and is developing into a flexible, user-friendly program with many benefits for Bayesian inference.

Suggested Citation

  • Sarah Depaoli & James P. Clifton & Patrice R. Cobb, 2016. "Just Another Gibbs Sampler (JAGS)," Journal of Educational and Behavioral Statistics, , vol. 41(6), pages 628-649, December.
    • Handle: RePEc:sae:jedbes:v:41:y:2016:i:6:p:628-649
    DOI: 10.3102/1076998616664876
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    References listed on IDEAS

    as
    1. Denwood, Matthew J., 2016. "runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i09).
    2. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    Full references (including those not matched with items on IDEAS)

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