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[ascl:1610.016] PyMC3: Python probabilistic programming framework

PyMC3 performs Bayesian statistical modeling and model fitting focused on advanced Markov chain Monte Carlo and variational fitting algorithms. It offers powerful sampling algorithms, such as the No U-Turn Sampler, allowing complex models with thousands of parameters with little specialized knowledge of fitting algorithms, intuitive model specification syntax, and optimization for finding the maximum a posteriori (MAP) point. PyMC3 uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed.

Code site:
https://github.com/pymc-devs/pymc3 http://pymc-devs.github.io/pymc3/
Appears in:
https://doi.org/10.7717/peerj-cs.55 http://adsabs.harvard.edu/abs/2016MNRAS.455.2656L http://adsabs.harvard.edu/abs/2016ApJ...827L..38G
Bibcode:
2016ascl.soft10016S
Preferred citation method:

https://doi.org/10.7717/peerj-cs.55


ascl:1610.016
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