利用者:KUBO Takuya/sandbox
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| 作者 | Stan Development Team |
|---|---|
| 初版 | 2012年8月30日 |
| 最新版 |
2.17.1
/ 2017年12月11日 |
| プログラミング 言語 | C++ |
| 対応OS | Unix-like, Microsoft Windows, Mac OS X |
| プラットフォーム | Intel x86 - 32-bit, x64 |
| サイズ | 41.2 MB |
| サポート状況 | Active |
| 種別 | Statistical package |
| ライセンス | New BSD License |
| 公式サイト |
mc-stan |
Stan is a probabilistic programming language for statistical inference written in C++.[1] The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function.[1]
'Stan は確率的プログラミング言語の一種で，C++ で実装されていて，2015 年に Stan 開発チームによって作られたものである．
Stan is licensed under the New BSD License. Stan is named in honour of Stanislaw Ulam, pioneer of the Monte Carlo method.[1]
Interfaces
[編集]Stan can be accessed through several interfaces:
- CmdStan - command-line executable for the shell
- RStan - integration with the R software environment
- PyStan - integration with the Python programming language
- MatlabStan - integration with the MATLAB numerical computing environment
- Stan.jl - integration with the Julia programming language
- StataStan - integration with Stata
Algorithms
[編集]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:
- No-U-Turn sampler[1][2] (NUTS), a variant of HMC and Stan's default MCMC engine
- Hamiltonian Monte Carlo
- Variational inference algorithms:
- Black-box Variational Inference[3]
- Optimization algorithms:
- Limited-memory BFGS (Stan's default optimization algorithm)
- Broyden–Fletcher–Goldfarb–Shanno algorithm
- Laplace's method for classical standard error estimates and approximate Bayesian posteriors
Automatic differentiation
[編集]Stan implements reverse-mode automatic differentiation to calculate gradients of the model, which is required by HMC, NUTS, L-BFGS, BFGS, and variational inference.[1] The automatic differentiation within Stan can be used outside of the probabilistic programming language.
Usage
[編集]Stan is used in fields including social science[4] and pharmaceutical statistics.[5]
References
[編集]- ^ a b c d e Stan Development Team. 2015. Stan Modeling Language User's Guide and Reference Manual, Version 2.9.0
- ^ 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. 1506.03431. arXiv:1506.03431. Bibcode: 2015arXiv150603431K.
- ^ 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 Template:SSRN
- ^ 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.
Further reading
[編集]- Bob, Carpenter; Andrew, Gelman; Matthew, Hoffman; Daniel, Lee; Ben, Goodrich; Michael, Betancourt; Marcus, Brubaker; Jiqiang, Guo et al. (2017). “Stan: A Probabilistic Programming Language”. Journal of Statistical Software 76 (1): 1–32. doi:10.18637/jss.v076.i01. ISSN 1548-7660.
- 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, Proceedings of the NIPS Workshop on Probabilistic Programming.
External links
[編集]- Stan web site
- Stan source, a Git repository hosted on GitHub