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DOC Update release notes.
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RELEASE-NOTES.md

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# Release Notes
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## PyMC3 3.0 (September xx, 2016)
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## PyMC3 3.0 (January 7, 2017)
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We are proud and excited to release the first stable version of PyMC3, the product of more than [5 years](https://github.com/pymc-devs/pymc3/commit/85c7e06b6771c0d99cbc09cb68885cda8f7785cb) 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.
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Andres Asensio Ramos
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Andrew Clegg <[email protected]>
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Anjum48
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AustinRochford <[email protected]>
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Austin Rochford <[email protected]>
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Benjamin Edwards <[email protected]>
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Boris Avdeev <[email protected]>
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Brian Naughton <[email protected]>
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Byron Smith
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Chad Heyne <[email protected]>
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Chris Fonnesbeck <[email protected]>
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Colin
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Colin Carroll
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Corey Farwell <[email protected]>
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David Huard <[email protected]>
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David Huard <[email protected]>
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Lin Xiao
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Mack Sweeney <[email protected]>
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Matthew Emmett <[email protected]>
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Maxim
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Maxim Kochurov
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Michael Gallaspy <[email protected]>
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Osvaldo Martin <[email protected]>
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Thomas Kluyver <[email protected]>
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Thomas Wiecki <[email protected]>
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Tobias Knuth <[email protected]>
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Volodymyr
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Volodymyr Kazantsev
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Wes McKinney <[email protected]>
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Zach Ploskey <[email protected]>
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ingmarschuster <[email protected]>
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jan-matthis <[email protected]>
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jason <JasonTam22@gmailcom>
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Jon Sedar <[email protected]>
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stonebig <stonebig>
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Tal Yarkoni <[email protected]>
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