PyMC3

PyMC3
Original author(s) PyMC3 Development Team
Initial release May 4, 2013 (2013-05-04)
Stable release
3.5 / July 21, 2018 (2018-07-21)
Written in Python
Operating system Unix-like, Mac OS X, Microsoft Windows
Platform Intel x86 – 32-bit, x64
Type Statistical package
License Apache License, Version 2.0
Website pymc-devs.github.io/pymc3/

PyMC3 is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.[1][2][3] It is a rewrite from scratch of the previous version of the PyMC software.[4] Unlike PyMC2, which had used Fortran extensions for performing computations, PyMC3 relies on Theano for automatic differentiation and also for computation optimization and dynamic C compilation.[2][5] PyMC3, together with STAN, are the most popular probabilistic programming tools.[6] PyMC3 is an open source project, developed by the community and fiscally sponsored by NumFocus.[7]

PyMC3 has been used to solve inference problems in several scientific domains, including astronomy,[8][9] molecular biology,[10] crystallography,[11][12] chemistry,[13] ecology[14][15] and psychology.[16] Previous versions of PyMC were also used widely, for example in climate science,[17] public health,[18] neuroscience,[19] and parasitology.[20][21]

Inference engines

PyMC3 implements non-gradient-based and gradient-based Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference and stochastic, gradient-based variational Bayesian methods for approximate Bayesian inference.

  • MCMC algorithms:
    • No-U-Turn sampler[22] (NUTS), a variant of Hamiltonian Monte Carlo and PyMC3's default engine for continuous variables
    • Metropolis–Hastings, PyMC3's default engine for discrete variables
    • Sequential Monte Carlo
  • Variational inference algorithms:
    • Black-box Variational Inference[23]

See also

  • STAN is a probabilistic programming language for statistical inference written in C++

References

  1. ↑ 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
  2. 1 2 Martin, Osvaldo (2016). Bayesian Analysis with Python. Packt Publishing Ltd. pp. 31–60. ISBN 9781785889851. Retrieved 16 September 2017.
  3. ↑ Davidson-Pilon, Cameron (2015-09-30). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional. ISBN 9780133902921.
  4. ↑ "Introduction — PyMC3 3.1 documentation". docs.pymc.io. Retrieved 2017-09-20.
  5. ↑ Hilpisch, Yves (2014-12-11). Python for Finance: Analyze Big Financial Data. O'Reilly Media, Inc. ISBN 9781491945391.
  6. ↑ "The Algorithms Behind Probabilistic Programming". Retrieved 2017-03-10.
  7. ↑ "NumFOCUS Announces New Fiscally Sponsored Project: PyMC3". NumFOCUS | Open Code = Better Science. Retrieved 2017-03-10.
  8. ↑ Greiner, J.; Burgess, J. M.; Savchenko, V.; Yu, H.-F. (2016). "On the Fermi-GBM Event 0.4 s after GW150914". The Astrophysical Journal Letters. 827 (2): L38. arXiv:1606.00314. Bibcode:2016ApJ...827L..38G. doi:10.3847/2041-8205/827/2/L38. ISSN 2041-8205.
  9. ↑ Hilbe, Joseph M.; Souza, Rafael S. de; Ishida, Emille E. O. (2017-04-30). Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan. Cambridge University Press. ISBN 9781108210744.
  10. ↑ 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 (2016-09-28). "Dose-Dependent Regulation of Alternative Splicing by MBNL Proteins Reveals Biomarkers for Myotonic Dystrophy". PLOS Genetics. 12 (9): e1006316. doi:10.1371/journal.pgen.1006316. ISSN 1553-7404.
  11. ↑ Sharma, Amit; Johansson, Linda; Dunevall, Elin; Wahlgren, Weixiao Y.; Neutze, Richard; Katona, Gergely (2017-03-01). "Asymmetry in serial femtosecond crystallography data". Acta Crystallographica Section A. 73 (2): 93–101. doi:10.1107/s2053273316018696. ISSN 2053-2733.
  12. ↑ Katona, Gergely; Garcia-Bonete, Maria-Jose; Lundholm, Ida (2016-05-01). "Estimating the difference between structure-factor amplitudes using multivariate Bayesian inference". Acta Crystallographica Section A. 72 (3): 406–411. doi:10.1107/S2053273316003430. ISSN 2053-2733. PMC 4850660.
  13. ↑ Garay, Pablo G.; Martin, Osvaldo A.; Scheraga, Harold A.; Vila, Jorge A. (2016-07-21). "Detection of methylation, acetylation and glycosylation of protein residues by monitoring13C chemical-shift changes: A quantum-chemical study". PeerJ. 4. doi:10.7717/peerj.2253. ISSN 2167-8359.
  14. ↑ 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. 152: 519–530. Bibcode:2017AtmEn.152..519W. doi:10.1016/j.atmosenv.2017.01.014.
  15. ↑ MacNeil, M. Aaron; Chong-Seng, Karen M.; Pratchett, Deborah J.; Thompson, Casssandra A.; Messmer, Vanessa; Pratchett, Morgan S. (2017-03-14). "Age and Growth of An Outbreaking Acanthaster cf. solaris Population within the Great Barrier Reef". Diversity. 9 (1): 18. doi:10.3390/d9010018.
  16. ↑ Tünnermann, Jan; Scharlau, Ingrid (2016). "Peripheral Visual Cues: Their Fate in Processing and Effects on Attention and Temporal-Order Perception". Frontiers in Psychology. 7. doi:10.3389/fpsyg.2016.01442. ISSN 1664-1078.
  17. ↑ Graham, Nicholas A. J.; Jennings, Simon; MacNeil, M. Aaron; Mouillot, David; Wilson, Shaun K. "Predicting climate-driven regime shifts versus rebound potential in coral reefs". Nature. 518 (7537): 94–97. Bibcode:2015Natur.518...94G. doi:10.1038/nature14140.
  18. ↑ Mascarenhas, Maya N.; Flaxman, Seth R.; Boerma, Ties; Vanderpoel, Sheryl; Stevens, Gretchen A. (2012-12-18). "National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Health Surveys". PLOS Medicine. 9 (12): e1001356. doi:10.1371/journal.pmed.1001356. ISSN 1549-1676.
  19. ↑ Cavanagh, James F; Wiecki, Thomas V; Cohen, Michael X; Figueroa, Christina M; Samanta, Johan; Sherman, Scott J; Frank, Michael J. "Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold". Nature Neuroscience. 14 (11): 1462–1467. doi:10.1038/nn.2925. PMC 3394226.
  20. ↑ Gething, Peter W.; Elyazar, Iqbal R. F.; Moyes, Catherine L.; Smith, David L.; Battle, Katherine E.; Guerra, Carlos A.; Patil, Anand P.; Tatem, Andrew J.; Howes, Rosalind E. (2012-09-06). "A Long Neglected World Malaria Map: Plasmodium vivax Endemicity in 2010". PLOS Neglected Tropical Diseases. 6 (9): e1814. doi:10.1371/journal.pntd.0001814. ISSN 1935-2735.
  21. ↑ Pullan, Rachel L.; Smith, Jennifer L.; Jasrasaria, Rashmi; Brooker, Simon J. (2014-01-21). "Global numbers of infection and disease burden of soil transmitted helminth infections in 2010". Parasites & Vectors. 7: 37. doi:10.1186/1756-3305-7-37. ISSN 1756-3305.
  22. ↑ 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&ndash, 1623.
  23. ↑ Kucukelbir, Alp; Ranganath, Rajesh; Blei, David M. (June 2015). "Automatic Variational Inference in Stan". 1506.03431. arXiv:1506.03431. Bibcode:2015arXiv150603431K.

Further reading

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