 | |
| Original author(s) | Stan Development Team |
|---|---|
| Initial release | August 30, 2012 |
| Stable release | 2.34.1[1] 
/ 23 January 2024 |
| Repository | |
| Written in | C++ |
| Operating system | Unix-like, Microsoft Windows, Mac OS X |
| Platform | Intel x86 - 32-bit, x64 |
| Type | Statistical package |
| License | New BSD License |
| Website | mc-stan |
Stan is a probabilistic programming language for statistical inference written in C++.[2] The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function.[2]
Stan is licensed under the New BSD License. Stan is named in honour of Stanislaw Ulam, pioneer of the Monte Carlo method.[2]
Stan was created by a development team consisting of 34 members[3] that includes Andrew Gelman, Bob Carpenter, Matt Hoffman, and Daniel Lee.
Interfaces[edit]
The Stan language itself can be accessed through several interfaces:
- CmdStan – a command-line executable for the shell,
- CmdStanR and rstan – R software libraries,
- CmdStanPy and PyStan – libraries for the Python programming language,
- CmdStan.rb - library for the Ruby programming language,
- MatlabStan – integration with the MATLAB numerical computing environment,
- Stan.jl – integration with the Julia programming language,
- StataStan – integration with Stata.
In addition, higher-level interfaces are provided with packages using Stan as backend, primarily in the R language:[4]
- rstanarm provides a drop-in replacement for frequentist models provided by base R and lme4 using the R formula syntax;
- brms[5] provides a wide array of linear and nonlinear models using the R formula syntax;
- prophet provides automated procedures for time series forecasting.
Algorithms[edit]
Stan implements gradient-based Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference, stochastic, gradient-based variational Bayesian methods for approximate Bayesian inference, and gradient-based optimization for penalized maximum likelihood estimation.
- MCMC algorithms:
- Hamiltonian Monte Carlo (HMC)
- No-U-Turn sampler[2][6] (NUTS), a variant of HMC and Stan's default MCMC engine
- Variational inference algorithms:
- Automatic Differentiation Variational Inference[7]
- Optimization algorithms:
- Limited-memory BFGS (Stan's default optimization algorithm)
- Broyden–Fletcher–Goldfarb–Shanno algorithm
- Laplace's approximation for classical standard error estimates and approximate Bayesian posteriors
Automatic differentiation[edit]
Stan implements reverse-mode automatic differentiation to calculate gradients of the model, which is required by HMC, NUTS, L-BFGS, BFGS, and variational inference.[2] The automatic differentiation within Stan can be used outside of the probabilistic programming language.
Usage[edit]
Stan is used in fields including social science,[8] pharmaceutical statistics,[9] market research,[10] and medical imaging.[11]
See also[edit]
- PyMC is a probabilistic programming language in Python
- ArviZ a Python library for Exploratory Analysis of Bayesian Models
References[edit]
- ^ "Release 2.34.1". 23 January 2024. Retrieved 20 February 2024.
- ^ a b c d e Stan Development Team. 2015. Stan Modeling Language User's Guide and Reference Manual, Version 2.9.0
- ^ "Development Team". stan-dev.github.io. Retrieved 2018-07-25.
- ^ Gabry, Jonah. "The current state of the Stan ecosystem in R". Statistical Modeling, Causal Inference, and Social Science. Retrieved 25 August 2020.
- ^ "BRMS: Bayesian Regression Models using 'Stan'". 23 August 2021.
- ^ Hoffman, Matthew D.; Gelman, Andrew (April 2014). "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo". Journal of Machine Learning Research. 15: pp. 1593–1623.
- ^ Kucukelbir, Alp; Ranganath, Rajesh; Blei, David M. (June 2015). "Automatic Variational Inference in Stan". 1506 (3431). arXiv:1506.03431. Bibcode:2015arXiv150603431K.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Goodrich, Benjamin King, Wawro, Gregory and Katznelson, Ira, Designing Quantitative Historical Social Inquiry: An Introduction to Stan (2012). APSA 2012 Annual Meeting Paper. Available at SSRN 2105531
- ^ Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W.; Kinnersley, Nelson; Heilmann, Cory R.; Ohlssen, David; Rochester, George (2013). "The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group". Pharmaceutical Statistics. 13 (1): 3–12. doi:10.1002/pst.1595. ISSN 1539-1612. PMID 24027093. S2CID 19738522.
- ^ Feit, Elea (15 May 2017). "Using Stan to Estimate Hierarchical Bayes Models". Retrieved 19 March 2019.
- ^ Gordon, GSD; Joseph, J; Alcolea, MP; Sawyer, T; Macfaden, AJ; Williams, C; Fitzpatrick, CRM; Jones, PH; di Pietro, M; Fitzgerald, RC; Wilkinson, TD; Bohndiek, SE (2019). "Quantitative phase and polarization imaging through an optical fiber applied to detection of early esophageal tumorigenesis". Journal of Biomedical Optics. 24 (12): 1–13. arXiv:1811.03977. Bibcode:2019JBO....24l6004G. doi:10.1117/1.JBO.24.12.126004. PMC 7006047. PMID 31840442.
Further reading[edit]
- Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew; Lee, Daniel; Goodrich, Ben; Betancourt, Michael; Brubaker, Marcus; Guo, Jiqiang; Li, Peter; Riddell, Allen (2017). "Stan: A Probabilistic Programming Language". Journal of Statistical Software. 76 (1): 1–32. doi:10.18637/jss.v076.i01. ISSN 1548-7660. PMC 9788645. PMID 36568334.
- Gelman, Andrew, Daniel Lee, and Jiqiang Guo (2015). Stan: A probabilistic programming language for Bayesian inference and optimization, Journal of Educational and Behavioral Statistics.
- Hoffman, Matthew D., Bob Carpenter, and Andrew Gelman (2012). Stan, scalable software for Bayesian modeling Archived 2015-01-21 at the Wayback Machine, Proceedings of the NIPS Workshop on Probabilistic Programming.
External links[edit]
- Stan web site
- Stan source, a Git repository hosted on GitHub
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