PyMC
PyMC（曾叫做PyMC3[2]）是一个Python包，用于贝叶斯统计建模和概率机器学习，它聚焦于高级马尔可夫链蒙特卡洛法和变分拟合算法[3][4][5]。
| 原作者 | PyMC开发团队 |
|---|---|
| 首次发布 | 2013年5月4日 |
| 目前版本 |
 |
| 源代码库 | https://github.com/pymc-devs/pymc |
| 编程语言 | Python |
| 操作系统 | 类Unix, Mac OS X, Microsoft Windows |
| 系统平台 | Intel x86 – 32-bit, x64 |
| 类型 | 统计包 |
| 许可协议 | Apache License, Version 2.0 |
| 网站 | www |
概述
PyMC曾经叫做PyMC3，不同于早先的使用Fortran扩展进行计算的PyMC2，依靠Theano来进行自动微分、计算优化和动态C编译[4][6]。从版本3.8开始PyMC依据ArviZ来处理绘图、分析和统计检查。PyMC和Stan是两个最流行的概率编程工具[7]。
PyMC是开源项目，由社区开发并在财务上得到NumFocus赞助[8]。PyMC已经在很多领域中被用于解决推断问题，包括天文学[9][10]、流行病学[11][12]、分子生物学[13]、晶体学[14][15]、化学[16]、生态学[17][18]和心理学[19]。
在Theano于2017年宣布计划停止开发之后[20]，PyMC团队评估采用TensorFlow Probability[21]作为计算后端[22]，但是在2020年决定接管Theano的开发[23]。在2021年1月绝大部分的Theano代码基被重造并通过JAX来编译[24]，并且增加了Numba，修订后的这个计算后端以新名字Aesara发行。PyMC团队决定在2021年6月将PyMC3更名为PyMC[2]。
推论引擎
PyMC实现了不基于梯度的和基于梯度的马尔可夫链蒙特卡洛（MCMC）算法用于贝叶斯推断和随机（Stochastic），基于梯度的变分贝叶斯方法用于近似贝叶斯推断。
- MCMC算法：
- No-U-Turn采样（NUTS）[25]，是哈密顿蒙特卡洛的变体和PyMC3用于连续变量的缺省引擎。
- 梅特罗波利斯－黑斯廷斯算法，是PyMC3用于离散变量的缺省引擎。
- 顺序蒙特卡洛算法。
- 变分推断算法：
- 黑箱变分推断[26]。
参见
- Stan是用C++编写统计推论的概率编程语言。
引用
- ^ Release 5.1.2. 2023年3月13日 [2023年3月15日].
- ^ 2.0 2.1 PyMC Timeline. PyMC Timeline. [2021-01-20]. （原始内容存档于2018-05-20）.
- ^ Salvatier J, Wiecki TV, Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 https://doi.org/10.7717/peerj-cs.55
- ^ 4.0 4.1 Martin, Osvaldo. Bayesian Analysis with Python. Packt Publishing Ltd. 2016: 31–60 [16 September 2017]. ISBN 9781785889851 （英语）.
- ^ Davidson-Pilon, Cameron. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional. 2015-09-30. ISBN 9780133902921 （英语）.
- ^ Hilpisch, Yves. Python for Finance: Analyze Big Financial Data. O'Reilly Media, Inc. 2014-12-11. ISBN 9781491945391 （英语）.
- ^ The Algorithms Behind Probabilistic Programming. [2017-03-10]. （原始内容存档于2021-01-29）.
- ^ NumFOCUS Announces New Fiscally Sponsored Project: PyMC3. NumFOCUS | Open Code = Better Science. [2017-03-10]. （原始内容存档于2017-09-21）.
- ^ Greiner, J.; Burgess, J. M.; Savchenko, V.; Yu, H.-F. On the Fermi-GBM Event 0.4 s after GW150914. The Astrophysical Journal Letters. 2016, 827 (2): L38. Bibcode:2016ApJ...827L..38G. ISSN 2041-8205. arXiv:1606.00314 . doi:10.3847/2041-8205/827/2/L38 （英语）.
- ^ Hilbe, Joseph M.; Souza, Rafael S. de; Ishida, Emille E. O. Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan. Cambridge University Press. 2017-04-30 [2021-01-20]. ISBN 9781108210744. （原始内容存档于2021-02-03） （英语）.
- ^ Brauner, Jan M.; Mindermann, Sören; Sharma, Mrinank; Johnston, David; Salvatier, John; Gavenčiak, Tom; Stephenson, Anna B.; Leech, Gavin; Altman, George; Mikulik, Vladimir; Norman, Alexander John; Monrad, Joshua Teperowski; Besiroglu, Tamay; Ge, Hong; Hartwick, Meghan A.; Teh, Yee Whye; Chindelevitch, Leonid; Gal, Yarin; Kulveit, Jan. Inferring the effectiveness of government interventions against COVID-19. Science. 2020-12-15 [2021-01-20]. doi:10.1126/science.abd9338. （原始内容存档于2021-02-07）.
- ^ Systrom, Kevin; Vladek, Thomas; Krieger, Mike. Rt.live Github repository. Rt.live. [10 January 2021]. （原始内容存档于2021-01-06）.
- ^ Wagner, Stacey D.; Struck, Adam J.; Gupta, Riti; Farnsworth, Dylan R.; Mahady, Amy E.; Eichinger, Katy; Thornton, Charles A.; Wang, Eric T.; Berglund, J. Andrew. Dose-Dependent Regulation of Alternative Splicing by MBNL Proteins Reveals Biomarkers for Myotonic Dystrophy. PLOS Genetics. 2016-09-28, 12 (9): e1006316. ISSN 1553-7404. PMC 5082313 . PMID 27681373. doi:10.1371/journal.pgen.1006316.
- ^ Sharma, Amit; Johansson, Linda; Dunevall, Elin; Wahlgren, Weixiao Y.; Neutze, Richard; Katona, Gergely. Asymmetry in serial femtosecond crystallography data. Acta Crystallographica Section A. 2017-03-01, 73 (2): 93–101. ISSN 2053-2733. PMC 5332129 . PMID 28248658. doi:10.1107/s2053273316018696 （英语）.
- ^ Katona, Gergely; Garcia-Bonete, Maria-Jose; Lundholm, Ida. Estimating the difference between structure-factor amplitudes using multivariate Bayesian inference. Acta Crystallographica Section A. 2016-05-01, 72 (3): 406–411. ISSN 2053-2733. PMC 4850660 . PMID 27126118. doi:10.1107/S2053273316003430 （英语）.
- ^ Garay, Pablo G.; Martin, Osvaldo A.; Scheraga, Harold A.; Vila, Jorge A. Detection of methylation, acetylation and glycosylation of protein residues by monitoring13C chemical-shift changes: A quantum-chemical study. PeerJ. 2016-07-21, 4: e2253. ISSN 2167-8359. PMC 4963218 . PMID 27547559. doi:10.7717/peerj.2253 （英语）.
- ^ Wang, Yan; Huang, Hong; Huang, Lida; Ristic, Branko. Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures. Atmospheric Environment. 2017, 152: 519–530. Bibcode:2017AtmEn.152..519W. doi:10.1016/j.atmosenv.2017.01.014.
- ^ MacNeil, M. Aaron; Chong-Seng, Karen M.; Pratchett, Deborah J.; Thompson, Casssandra A.; Messmer, Vanessa; Pratchett, Morgan S. Age and Growth of An Outbreaking Acanthaster cf. solaris Population within the Great Barrier Reef. Diversity. 2017-03-14, 9 (1): 18. doi:10.3390/d9010018 （英语）.
- ^ Tünnermann, Jan; Scharlau, Ingrid. Peripheral Visual Cues: Their Fate in Processing and Effects on Attention and Temporal-Order Perception. Frontiers in Psychology. 2016, 7. ISSN 1664-1078. PMC 5052275 . PMID 27766086. doi:10.3389/fpsyg.2016.01442 （英语）.
- ^ Lamblin, Pascal. MILA and the future of Theano. theano-users (邮件列表). 28 September 2017 [28 September 2017]. （原始内容存档于2011-01-22）.
- ^ TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. [2022-08-31]. （原始内容存档于2022-09-04）.
- ^ Developers, PyMC. Theano, TensorFlow and the Future of PyMC. PyMC Developers. 2018-05-17 [2019-01-25]. （原始内容存档于2020-08-06）.
- ^ The Future of PyMC3, or: Theano is Dead, Long Live Theano. PyMC Developers. [10 January 2021]. （原始内容存档于2021-01-15）.
- ^ Bradbury, James; Frostig, Roy; Hawkins, Peter; James, Matthew James; Leary, Chris; Maclaurin, Dougal; Necula, George; Paszke, Adam; VanderPlas, Jake; Wanderman-Milne, Skye; Zhang, Qiao. JAX. [10 January 2021]. （原始内容存档于2021-02-05）.
- ^ Hoffman, Matthew D.; Gelman, Andrew. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. April 2014, 15: pp. 1593–1623 [2021-01-20]. （原始内容存档于2020-08-11）.
- ^ Kucukelbir, Alp; Ranganath, Rajesh; Blei, David M. Automatic Variational Inference in Stan 1506 (3431). June 2015. Bibcode:2015arXiv150603431K. arXiv:1506.03431 .
延伸阅读
外部链接
- PyMC web site （页面存档备份，存于互联网档案馆）
- PyMC source （页面存档备份，存于互联网档案馆）, a Git repository hosted on GitHub
- Symbolic PyMC （页面存档备份，存于互联网档案馆） is an experimental set of tools that facilitate sophisticated symbolic manipulation of PyMC models