Publication: Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization
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Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization

- Article in a journal -
 

Area
Machine Learning, Statistics

Author(s)
Andrew Gelman , Daniel Lee , Jiqiang Guo

Published in
Journal of Educational and Behavioral Statistics

Year
2015

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.

BibTeX
@ARTICLE{
         Gelman2015SAP,
       author = "Andrew Gelman and Daniel Lee and Jiqiang Guo",
       title = "Stan: A Probabilistic Programming Language for {B}ayesian Inference and Optimization",
       journal = "Journal of Educational and Behavioral Statistics",
       volume = "40",
       number = "5",
       pages = "530--543",
       year = "2015",
       doi = "10.3102/1076998615606113",
       url = "http://dx.doi.org/10.3102/1076998615606113",
       eprint = "http://dx.doi.org/10.3102/1076998615606113",
       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.",
       ad_area = "Machine Learning, Statistics"
}


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