Probabilistic programming in Python using PyMC3

Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information whic...

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Published in	PeerJ. Computer science Vol. 2; p. e55
Main Authors	Salvatier, John, Wiecki, Thomas V., Fonnesbeck, Christopher
Format	Journal Article
Language	English
Published	San Diego PeerJ, Inc 06.04.2016 PeerJ Inc
Subjects	Advantages Algorithms Bayesian analysis Bayesian statistic C plus plus Computer simulation Domain specific languages Generalized linear models Markov analysis Markov chain Monte Carlo Markov chains Monte Carlo simulation Probabilistic inference Probabilistic models Probabilistic Programming Probability theory Programming languages Python Regression analysis Software Statistical inference Statistical modeling Syntax
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Abstract	Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.
AbstractList	Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.
ArticleNumber	e55
Author	Salvatier, John Fonnesbeck, Christopher Wiecki, Thomas V.
Author_xml	– sequence: 1 givenname: John surname: Salvatier fullname: Salvatier, John organization: AI Impacts, Berkeley, CA, United States – sequence: 2 givenname: Thomas V. surname: Wiecki fullname: Wiecki, Thomas V. organization: Quantopian Inc, Boston, MA, United States – sequence: 3 givenname: Christopher surname: Fonnesbeck fullname: Fonnesbeck, Christopher organization: Department of Biostatistics, Vanderbilt University, Nashville, TN, United States
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References_xml	– start-page: 2 volume-title: Proceedings of the 17th international conference on artificial intelligence and statistics year: 2014 ident: 10.7717/peerj-cs.55/ref-20 article-title: A new approach to probabilistic programming inference – year: 2014 ident: 10.7717/peerj-cs.55/ref-11 article-title: Venture: a higher-order probabilistic programming platform with programmable inference – volume-title: Infer.NET 2.6 year: 2010 ident: 10.7717/peerj-cs.55/ref-12 – year: 2015 ident: 10.7717/peerj-cs.55/ref-10 article-title: Picture: an imperative probabilistic programming language for scene perception doi: 10.1109/CVPR.2015.7299068 – volume: vol. 4 volume-title: Proceedings of the Python for scientific computing conference (SciPy) year: 2010 ident: 10.7717/peerj-cs.55/ref-3 article-title: Theano: a CPU and GPU math expression compiler – year: 2014 ident: 10.7717/peerj-cs.55/ref-13 article-title: Figaro: a language for probabalistic programming – volume: 13 start-page: 31 issue: 2 year: 2011 ident: 10.7717/peerj-cs.55/ref-2 article-title: Cython: the best of both worlds publication-title: Computing in Science & Engineering doi: 10.1109/MCSE.2010.118 – volume: 13 start-page: 22 year: 2011 ident: 10.7717/peerj-cs.55/ref-19 article-title: The NumPy Array: a structure for efficient numerical computation publication-title: Computing in Science & Engineering doi: 10.1109/MCSE.2011.37 – volume: 195 start-page: 216 issue: 2 year: 1987 ident: 10.7717/peerj-cs.55/ref-4 article-title: Hybrid Monte Carlo publication-title: Physics Letters B doi: 10.1016/0370-2693(87)91197-X – volume: 15 start-page: 1593 year: 2014 ident: 10.7717/peerj-cs.55/ref-7 article-title: The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo publication-title: Journal of Machine Learning Research – year: 2012 ident: 10.7717/peerj-cs.55/ref-5 article-title: Church: a language for generative models – year: 2012 ident: 10.7717/peerj-cs.55/ref-1 article-title: Theano: new features and speed improvements – start-page: 568 volume-title: Advances in neural information processing systems year: 2015 ident: 10.7717/peerj-cs.55/ref-9 article-title: Automatic variational inference in Stan – volume-title: Bugs: Bayesian inference using Gibbs sampling, version 0.50 year: 1995 ident: 10.7717/peerj-cs.55/ref-16 – year: 2010 ident: 10.7717/peerj-cs.55/ref-14 publication-title: Python language reference – year: 2014 ident: 10.7717/peerj-cs.55/ref-6 article-title: The design and implementation of probabilistic programming languages – volume: 66 start-page: 191 issue: 1 year: 1979 ident: 10.7717/peerj-cs.55/ref-8 article-title: A note on the intervals between coal-mining disasters publication-title: Biometrika doi: 10.1093/biomet/66.1.191 – year: 2014 ident: 10.7717/peerj-cs.55/ref-15 article-title: Quicksand: a low-level probabilistic programming framework embedded in Terra – year: 2014 ident: 10.7717/peerj-cs.55/ref-18 article-title: Frequentism and bayesianism: a python-driven primer doi: 10.25080/Majora-14bd3278-00e – volume-title: Stan: a c++ library for probability and sampling year: 2015 ident: 10.7717/peerj-cs.55/ref-17
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Snippet	Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC)...
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SubjectTerms	Advantages Algorithms Bayesian analysis Bayesian statistic C plus plus Computer simulation Domain specific languages Generalized linear models Markov analysis Markov chain Monte Carlo Markov chains Monte Carlo simulation Probabilistic inference Probabilistic models Probabilistic Programming Probability theory Programming languages Python Regression analysis Software Statistical inference Statistical modeling Syntax
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