External References

External References

the word on the street

Books with Examples Translated to Stan

  • Eric-Jan Wagenmakers and Michael D. Lee. 2014. Bayesian Cognitive Modeling: A Practical Course. Cambridge Univesity Press.

  • Lunn, D., Jackson, C., Best, N., Thomas, A., and Spiegelhalter, D. 2013. The BUGS Book: A Practical Introduction to Bayesian Analysis. Chapman & Hall/CRC Press.

  • Marc Kéry and Michael Schaub. 2011. Bayesian Population Analysis using WinBUGS: A hierarchical perspective. Academic Press. [chapters 3–10]

  • Andrew Gelman and Jennifer Hill. 2009. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. [second edition being updated to 100% Stan implementations]

Code may be found in the Stan example models repo.

Papers about Stan

  • Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. Stan: A probabilistic programming language. Journal of Statistical Software 76(1). 10.18637/jss.v076.i01

  • Aki Vehtari, Andrew Gelman, and Jonah Gabry. 2016. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. doi:10.1007/s11222-016-9696-4.

  • Robert L. Grant, Daniel C. Furr, Bob Carpenter, and Andrew Gelman. 2016. Fitting Bayesian item response models in Stata and Stan. arXiv 1601.03443.

  • Andrew Gelman, Daniel Lee, and Jiqiang Guo. 2015. Stan: A probabilistic programming language for Bayesian inference and optimization. Journal of Education and Behavioral Statistics. 40(5):530–543.

  • Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman and David M. Blei. 2015. Automatic Variational Inference in Stan, NIPS.

  • Bob Carpenter, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael J. Betancourt. 2015. The Stan Math Library: Reverse-Mode Automatic Differentiation in C++. arXiv 1509.07164.

Papers About Hamiltonian Monte Carlo

  • Michael Betancourt. 2017. A Conceptual Introduction to Hamiltonian Monte Carlo. arXiv:1701.02434.

  • Cole C. Monnahan, James T. Thorson, and Trevor A. Branch. 2016. Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods in Ecology and Evolution.

  • Michael J. Betancourt. 2016. Identifying the Optimal Integration Time in Hamiltonian Monte Carlo. arXiv:1601.00225.

  • Matthew D. Hoffman and Andrew Gelman. 2014. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. 15(Apr):1593–1623.

  • Michael J. Betancourt, Mark Girolami. 2013. Hamiltonian Monte Carlo for Hierarchical Models. arXiv 1312.0906.

  • Michael J. Betancourt. 2013. A General Metric for Riemannian Manifold Hamiltonian Monte Carlo. arXiv 1212.4693.

  • Michael J. Betancourt. 2013. Generalizing the No-U-Turn Sampler to Riemannian Manifolds. arXiv 1304.1920.

  • Radford Neal. 2011. http://www.mcmchandbook.net/HandbookChapter5.pdf. In Handbook of Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng. Chapman-Hall/CRC.

Books using Stan

  • Hilbe, J. M., R.S. de Souza, and E. E. O. Ishida. 2017. Bayesian Models for Astrophysical Data Using R, JAGS, Python and Stan. Cambridge University Press.

  • Matsuura, K.. 2016. Bayesian Statistical Modeling Using Stan and R. Wonderful R Series, Volume 2. Kyoritsu Shuppan Co., Ltd. [in Japanese]

  • McElreath, R. 2016. Statistical Rethinking: A Bayesian Course with R and Stan. Chapman-Hall/CRC.

  • Korner-Nievergelt, F., Roth, T., Von Felten, S., Guélat, J., Almasi, B. and Korner-Nievergelt, P. 2015. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press.

  • Kruschke, J. 2014. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press.

  • Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. and Rubin, D.B. 2013. Bayesian Data Analysis, Third Edition. Chapman-Hall/CRC.

Software using Stan

  • McElreath, R.   rethinking: Statistical Rethinking book package, version 1.58. GitHub project rmcelreath/rethinking. Language: R

  • Bürkner, P.-C.   brms: Bayesian Regression Models using Stan. CRAN package brms. Language: R

  • Facebook.   PROPHET: Forecasting at Scale. Languages: R and Python

Additional CRAN packages using Stan in R can be found in the reverse links from the packages rstan and rstanarm.

Software using the No-U-Turn Sampler

  • ICON, plc. 2017. NONMEM 7.4, Nonlinear mixed effects models for pharmacometrics. [commercial paid license, not open source]

  • PyMC Developers. 2016. PyMC3: Probabilistic programming in Python. GitHub project pymc-devs/pymc3.