Just Another Gibbs Sampler (JAGS)

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.

[1]  Péter Sólymos,et al.  dclone: Data Cloning in R , 2010, R J..

[2]  B. Beersma,et al.  A Temporal Map of Coaching , 2017, Frontiers in Psychology.

[3]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[4]  Finn Lindgren,et al.  Bayesian Spatial Modelling with R-INLA , 2015 .

[5]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[6]  Andrew Thomas,et al.  The BUGS project: Evolution, critique and future directions , 2009, Statistics in medicine.

[7]  Jiqiang Guo,et al.  Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.

[8]  Radford M. Neal MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.

[9]  Simon Jackman,et al.  Bayesian Analysis for the Social Sciences , 2009 .

[10]  Russell G. Almond,et al.  A Comparison of Two MCMC Algorithms for Hierarchical Mixture Models , 2014, BMA@UAI.

[11]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[12]  Joseph Hilbe,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .

[13]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[14]  Duncan Temple Lang,et al.  Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE , 2015, 1505.05093.

[15]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[16]  Ramesh Srinivasan,et al.  Individual differences in attention influence perceptual decision making , 2015, Front. Psychol..

[17]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[18]  David B Dunson,et al.  Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis , 2009, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[19]  A. Gelman,et al.  Stan , 2015 .

[20]  Stephen J. Roberts,et al.  A tutorial on variational Bayesian inference , 2012, Artificial Intelligence Review.

[21]  Matthew J. Denwood,et al.  runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS , 2016 .

[22]  Joachim Vandekerckhove,et al.  Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example) , 2013, Behavior Research Methods.

[23]  M. Plummer JAGS Version 4.0.0 user manual , 2015 .