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Probabilistic Programming in Python using PyMC

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arxiv 1507.08050 v1 pith:WF3T2MDN submitted 2015-07-29 stat.CO

John Salvatier , Thomas Wiecki , Christopher Fonnesbeck This is my paper · ORCID
classification stat.CO
keywords probabilisticprogrammingsyntaxbayesiancarlofeaturesmodelsmonte
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Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.

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Cited by 5 Pith papers

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