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Stan

Author

Listed:
  • Andrew Gelman

    (Columbia University)

  • Daniel Lee

    (Columbia University)

  • Jiqiang Guo

    (Columbia University)

Abstract

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users’ and developers’ perspectives and illustrate with a simple but nontrivial nonlinear regression example.

Suggested Citation

  • Andrew Gelman & Daniel Lee & Jiqiang Guo, 2015. "Stan," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 530-543, October.
    • Handle: RePEc:sae:jedbes:v:40:y:2015:i:5:p:530-543
    DOI: 10.3102/1076998615606113
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    References listed on IDEAS

    as
    1. Patil, Anand & Huard, David & Fonnesbeck, Christopher J., 2010. "PyMC: Bayesian Stochastic Modelling in Python," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i04).
    2. Rabe-Hesketh, Sophia & Skrondal, Anders & Pickles, Andrew, 2005. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects," Journal of Econometrics, Elsevier, vol. 128(2), pages 301-323, October.
    3. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    Full references (including those not matched with items on IDEAS)

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