PyMC

PyMC（曾叫做PyMC3）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。 PyMC曾经叫做PyMC3，不同于早先的使用Fortran扩展进行计算的PyMC2，它依靠Theano来进行自动微分、计算优化和动态C语言编译。从版本3.8开始PyMC依据ArviZ（英语：ArviZ）来进行数据可视化和贝叶斯推断的探索分析（英语：Exploratory data analysis）。PyMC和Stan（英语：Stan (software)）是两个最流行的概率编程工具。 PyMC是开源项目，由社区开发并在财务上得到NumFocus赞助。PyMC已经在很多领域中被用于解决推断问题，包括天文学、流行病学、分子生物学、晶体学、化学、生态学和心理学。 在Theano于2017年宣布计划停止开发之后，PyMC团队曾评估采用TensorFlow Probability作为计算后端，但是在2020年接管Theano的开发。在2021年1月绝大部份的Theano代码基被重新建造，并增加了通过JAX和Numba的编译，修订后的这个计算后端以新名字Aesara发行。PyMC团队在2021年6月将PyMC3更名为PyMC。2022年11月28日，PyMC团队宣布采用从Aesara计划分叉出PyTensor。 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（英语：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），是哈密顿蒙特卡洛（英语：Hamiltonian Monte Carlo）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。 Stan（英语：Stan (software)）是用C++编写统计推论的概率编程语言。 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 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）.  ArviZ — Exploratory analysis of Bayesian models. [2023-09-21]. （原始内容存档于2023-10-11）.  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. 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[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 . 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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 .  Probabilistic Programming and Bayesian Methods for Hackers （页面存档备份，存于互联网档案馆） Computational Statistics in Python （页面存档备份，存于互联网档案馆） 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