Probabilistic programming in Python using PyMC3

PeerJ Computer Science

Volumn 2016, Issue 4, 2016, Pages

  Salvatier, John ^a   Wiecki, Thomas V ^b   Fonnesbeck, Christopher ^c  

^a AI Impacts   (United States)

^b Quantopian Inc   (United States)

^c VANDERBILT UNIVERSITY   (United States)

Author keywords

Bayesian statistic; Markov chain Monte Carlo; Probabilistic Programming; Python; Statistical modeling

Indexed keywords

BAYESIAN NETWORKS; CHAINS; INFERENCE ENGINES; MARKOV PROCESSES; MONTE CARLO METHODS; OPEN SOURCE SOFTWARE; PROBLEM ORIENTED LANGUAGES;

BAYESIAN STATISTICS; MARKOV CHAIN MONTE-CARLO; PROBABILISTIC PROGRAMMING; PYTHON; STATISTICAL MODELING;

C (PROGRAMMING LANGUAGE);

EID: 84990201464     PISSN: None     EISSN: 23765992     Source Type: Journal    
DOI: 10.7717/peerj-cs.55     Document Type: Article

References (20)

1. Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow I, Bergeron A, Bouchard N, Warde-Farley D, Bengio Y. 2012. Theano: new features and speed improvements. ArXiv preprint. arXiv:1211.5590.
2. Behnel S, Bradshaw R, Citro C, Dalcin L, Seljebotn DS, Smith K. 2011. Cython: the best of both worlds. Computing in Science & Engineering 13(2):31-39 DOI 10.1109/MCSE.2010.118.
3. Bergstra J, Breuleux O, Bastien F, Lamblin P, Pascanu R, Desjardins G, Turian J, Warde-Farley D, Bengio Y. 2010. Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for scientific computing conference (SciPy), vol. 4. Austin: SciPy, 3. Available at http:// www.iro.umontreal.ca/ ~lisa/ pointeurs/ theano_ scipy2010.pdf .
4. Duane S, Kennedy AD, Pendleton BJ, Roweth D. 1987. Hybrid Monte Carlo. Physics Letters B 195(2):216-222 DOI 10.1016/0370-2693(87)91197-X.
5. Goodman N, Mansinghka V, Roy D, Bonawitz K, Tarlow D. 2012. Church: a language for generative models. ArXiv preprint. arXiv:1206.3255.
6. Goodman ND, Stuhlmüller A. 2014. The design and implementation of probabilistic programming languages. Available at http:// webppl.org/ .
7. Hoffman MD, Gelman A. 2014. The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15:1593-1623.
8. Jarrett R. 1979. A note on the intervals between coal-mining disasters. Biometrika 66(1):191-193 DOI 10.1093/biomet/66.1.191.
9. Kucukelbir A, Ranganath R, Gelman A, Blei D. 2015. Automatic variational inference in Stan. In: Advances in neural information processing systems. Red Hook: Curran & Associates, Inc., 568-576.
10. Kulkarni T, Kohli P, Tenenbaum J, Mansinghka V. 2015. Picture: an imperative probabilistic programming language for scene perception. Available at https:// mrkulk.github.io/www_cvpr15/1999.pdf .
11. Mansinghka V, Selsam D, Perov Y. 2014. Venture: a higher-order probabilistic program- ming platform with programmable inference. ArXiv preprint. arXiv:1404.0099v1.
12. Minka T, Winn J, Guiver J, Knowles D. 2010. Infer.NET 2.6. Cambridge: Microsoft Research.
13. Pfeffer A. 2014. Figaro: a language for probabalistic programming. Available at http: // www.github.com/ p2t2/ figaro.
14. Python Software Foundation. 2010. Python language reference. Version 2.7. Available at http:// www.python.org .
15. Ritchie D. 2014. Quicksand: a low-level probabilistic programming framework embed- ded in Terra. Available at http:// dritchie.github.io/ quicksand/ .
16. Spiegelhalter DJ, Thomas A, Best NG, Gilks WR. 1995. Bugs: Bayesian inference using Gibbs sampling, version 0.50. Cambridge: MRC Biostatistics Unit.
17. Stan Development Team. 2015. Stan: a c++ library for probability and sampling . Version 2.5. Available at http://mc-stan.org/ .
18. VanderPlas J. 2014. Frequentism and bayesianism: a python-driven primer. ArXiv preprint. arXiv:1411.5018.
19. Van der Walt S, Colbert SC, Varoquaux G. 2011. The NumPy Array: a structure for efficient numerical computation. Computing in Science & Engineering 13:22-30.
20. Wood F, Van de Meent JW, Mansinghka V. 2014. A new approach to probabilistic programming inference. In: Proceedings of the 17th international conference on artificial intelligence and statistics, 2-46.