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* improve appearance
* get tags and categories to show in all pages, not only posts
* further improvements
* try adding codeautolink
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Supporting examples and tutorials for PyMC3, the Python package for Bayesian statistical modeling and Probabilistic Machine Learning!
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Supporting examples and tutorials for PyMC, the Python package for Bayesian statistical modeling and Probabilistic Machine Learning!
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Check out the `getting started guide <http://docs.pymc.io/notebooks/getting_started>`__, or
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`interact with live examples <https://mybinder.org/v2/gh/pymc-devs/pymc3/master?filepath=%2Fdocs%2Fsource%2Fnotebooks>`__
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using Binder!
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For questions on PyMC3, head on over to our `PyMC Discourse <https://discourse.pymc.io/>`__ forum.
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interact with live examples using Binder! Each notebook in `PyMC examples gallery
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<https://pymc-examples.readthedocs.io>`__ has a binder badge.
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For questions on PyMC, head on over to our `PyMC Discourse <https://discourse.pymc.io/>`__ forum.
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Contributing
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============
@@ -26,15 +26,15 @@ Contact
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We are using `discourse.pymc.io <https://discourse.pymc.io/>`__ as our main communication channel. You can also follow us on `Twitter @pymc_devs <https://twitter.com/pymc_devs>`__ for updates and other announcements.
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To ask a question regarding modeling or usage of PyMC3 we encourage posting to our Discourse forum under the `“Questions” Category <https://discourse.pymc.io/c/questions>`__. You can also suggest feature in the `“Development” Category <https://discourse.pymc.io/c/development>`__.
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To ask a question regarding modeling or usage of PyMC we encourage posting to our Discourse forum under the `“Questions” Category <https://discourse.pymc.io/c/questions>`__. You can also suggest feature in the `“Development” Category <https://discourse.pymc.io/c/development>`__.
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To report an issue, please use the following:
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- `PyMC3 Examples - Issue Tracker <https://github.com/pymc-devs/pymc-examples/issues>`__. For
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- `PyMC Examples - Issue Tracker <https://github.com/pymc-devs/pymc-examples/issues>`__. For
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issues about the example notebooks, errors in the example codes, outdated information, improvement
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suggestions...
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- `PyMC3 - Issue Tracker <https://github.com/pymc-devs/pymc3/issues>`__. For issues, bugs or
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feature requests related to the PyMC3 library itself.
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- `PyMC - Issue Tracker <https://github.com/pymc-devs/pymc/issues>`__. For issues, bugs or
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feature requests related to the PyMC library itself.
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Finally, if you need to get in touch for non-technical information about the project, `send us an e-mail <[email protected]>`__.
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- `Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>`__ by Cameron Davidson-Pilon: Fantastic book with many applied code examples.
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- `Doing Bayesian Data Analysis <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__ by John Kruschke, as well as the `second edition <https://github.com/JWarmenhoven/DBDA-python>`__: Principled introduction to Bayesian data analysis.
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- `Statistical Rethinking: A Bayesian Course with Examples in R and Stan <https://github.com/pymc-devs/resources/tree/master/Rethinking>`__ by Richard McElreath: Comprehensive text on modeling choices and interpretations.
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- `Statistical Rethinking: A Bayesian Course with Examples in R and Stan <https://github.com/pymc-devs/resources/tree/master/Rethinking>`__ by Richard McElreath: Comprehensive text on modeling choices and interpretations.
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- `Bayesian Cognitive Modeling <https://github.com/pymc-devs/resources/tree/master/BCM>`__ by Michael Lee and EJ Wagenmakers: Focused on using Bayesian statistics in cognitive modeling.
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- `Bayesian Analysis with Python <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition>`__ (second edition) by Osvaldo Martin: Great introductory book. (`code <https://github.com/aloctavodia/BAP>`__ and errata).
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PyMC3 talks
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PyMC talks
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-----------
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There are also several talks on PyMC3 which are gathered in this `YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>`__
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There are also several talks on PyMC which are gathered in this `YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>`__
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and as part of `PyMCon 2020 <https://discourse.pymc.io/c/pymcon/2020talks/15>`__
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Installation
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------------
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To install PyMC3 on your system, see its `installation section here <https://github.com/pymc-devs/pymc3#installation>`__
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To install PyMC on your system, see its `installation section here <https://docs.pymc.io/en/stable/>`__
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Citing PyMC3
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Citing PyMC
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============
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Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming
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in Python using PyMC3. PeerJ Computer Science 2:e55
* To cite specific guides from this collection, use |zenodo|. You'll find page specific
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citation instructions at the bottom of each page.
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Papers citing PyMC
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-------------------
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See `Google Scholar <https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011>`__ for a continuously updated list.
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Support
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=======
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PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate `here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>`__.
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PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate `here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>`__.
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