Probabilistic programming (PP) allows for flexible specification and fitting of Bayesian statistical models. Probabilistic programming in Python using PyMC3. ![]() Cite this article Salvatier J, Wiecki TV, Fonnesbeck C. ![]() For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. ![]() 3 Department of Biostatistics, Vanderbilt University, Nashville, TN, United States DOI 10.7717/peerj-cs.55 Published Accepted Received Academic Editor Charles Elkan Subject Areas Data Mining and Machine Learning, Data Science, Scientific Computing and Simulation Keywords Bayesian statistic, Probabilistic Programming, Python, Markov chain Monte Carlo, Statistical modeling Copyright © 2016 Salvatier et al.
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