[ANN] PyMC3 3.0 released

[Thomas Wiecki]

We are proud and excited to release the first stable version of PyMC3, the product of more than 5 years of ongoing development and contributions from over 80 individuals. PyMC3 is a Python module for Bayesian modeling which focuses on modern Bayesian computational methods, primarily gradient-based (Hamiltonian) MCMC sampling and variational inference. Models are specified in Python, which allows for great flexibility. The main technological difference in PyMC3 relative to previous versions is the reliance on Theano for the computational backend, rather than on Fortran extensions.

What is it?

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PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

Links

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Release announcement: https://github.com/pymc-devs/pymc3/blob/master/RELEASE-NOTES.md#pymc3-30-january-9-2017

Homepage: http://pymc-devs.github.io/pymc3/

GitHub: https://github.com/pymc-devs/pymc3

Installation

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pip install pymc3

or

conda install -c conda-forge pymc3

Regards,
The PyMC3 development team