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.gitignore

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# Sphinx
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_build
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docs/jupyter_execute
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# Merge tool
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*.orig

conda-envs/environment-dev-py37.yml

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- recommonmark>=0.4
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- scipy>1.4.1
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- sphinx-autobuild>=0.7
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- sphinx-panels
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- sphinx>=1.5
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- typing-extensions
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conda-envs/environment-dev-py38.yml

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- sphinx-panels
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- sphinx-design
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- sphinx>=1.5
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- typing-extensions>=3.7.4
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conda-envs/environment-dev-py39.yml

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- sphinx-panels
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- sphinx-design
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- sphinx>=1.5
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- typing-extensions>=3.7.4
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conda-envs/windows-environment-dev-py38.yml

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- recommonmark>=0.4
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- sphinx-design
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docs/source/community.md

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# Community
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# Learn Bayesian statistics
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Interact with others in the PyMC world
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## ...with others!
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## Events
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:::{list-table}
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:header-rows: 0
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:widths: 10 150
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### Journal Club
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* - {fa}`discourse,fa-2x,style=fab`
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- [Discourse Forum](https://discourse.pymc.io/)
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Every month the PyMC developers meet virtually to talk about a recent or particularly interesting article.
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The PyMC discourse forum is a great place to ask general questions about Bayesian statistics, or more specific ones about PyMC usage.
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* - {fa}`meetup,fa-2x,style=fab`
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- Conferences
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Find out more [here](https://discourse.pymc.io/c/journal-club/9)!
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PyMC talks have been given at a number of conferences, including [PyCon](https://us.pycon.org/),
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[PyData](https://pydata.org/events/), and [ODSC](https://odsc.com/) events.
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* - {fa}`python,fa-2x,style=fab`
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- [Meetup Groups](https://www.meetup.com/)
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Many areas have an local Bayesian, PyData, or Stan meetup.
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:::
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## ...with a video!
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### PyMCon
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:::{list-table}
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:header-rows: 0
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:widths: 10 150
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Check out information about our past conference, [PyMCon 2020](https://pymcon.com)
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* - {fa}`youtube,fa-2x,style=fab`
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- [YouTube Playlist](https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy)
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The next edition will likely be held in 2022. Follow us on Twitter for announcements!
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There is an actively curated playlist of PyMC talks on YouTube.
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* - {fa}`youtube,fa-2x,style=fab`
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- [Advance Bayesian Modelling with PyMC](https://github.com/junpenglao/advance-bayesian-modelling-with-PyMC)
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A two days advance workshop in May 2018 @CEAi in the Czech Republic.
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:::
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## Code of Conduct
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## ...with a book!
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In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
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::::{panels}
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:column: col-lg-4 col-md-4
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Read more and find out how to report an issue [here](https://github.com/pymc-devs/pymc/blob/main/CODE_OF_CONDUCT.md).
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---
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:img-top: https://camo.githubusercontent.com/4a0aca82ca82efab71747d00db30f3a68de98e82/687474703a2f2f692e696d6775722e636f6d2f36444b596250622e706e673f31
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## Twitter
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Bayesian Methods for Hackers
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::::{card}
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:text-align: center
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*Cameron Davidson-Pilon*
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^^^^^^^^
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:::{raw} html
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Fantastic book with many applied code examples.
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<a class="twitter-timeline" data-width="300" data-height="500" data-dnt="true" href="https://twitter.com/pymc_devs?ref_src=twsrc%5Etfw">Tweets by pymc_devs</a> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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:::{list-table}
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:header-rows: 0
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:widths: 4 60
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* - [{fa}`link`][hackers source]
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- [Github Repo][hackers source]
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* - [{fa}`link`][hackers homepage]
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- [Project Homepage][hackers homepage]
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::::
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::::{grid} 2
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:gutter: 3 3 4 5
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---
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:img-top: https://lh3.googleusercontent.com/qU5yWpiVSkujyCnRZhQo8cBUrW_3e_7Cx4jEs5yfpmMbEEuDnxTGOELhy2x7F7qkwFwQJ4pFYNszZhNpN1jCSdrdHsVUHtf1Mg6F8qwb3SQe1TDE=w1280
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:::{grid-item-card} Need help?
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[Ask a question!](https://discourse.pymc.io/)
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Doing Bayesian Data Analysis
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[Read the User Docs]()
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*John Kruschke*
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^^^^^^^^
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[Read the Developer Docs]()
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Principled introduction to Bayesian data analysis.
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:::{list-table}
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:header-rows: 0
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:widths: 4 60
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* - [{fa}`link`][dbda website]
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- [Book Website][dbda website]
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* - [{fa}`link`][dbda 1st ed nb]
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- [PyMC notebooks for *first edition*][dbda 1st ed nb]
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* - [{fa}`link`][dbda 2nd ed nb]
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- [PyMC notebooks for *second edition*][dbda 2nd ed nb]
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:::{grid-item-card} Around the web
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[Github](https://github.com/pymc-devs)
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[Twitter](http://https://twitter.com/pymc_devs)
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---
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:img-top: http://xcelab.net/rm/wp-content/uploads/2012/01/9781482253443-191x300.jpg
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Statistical Rethingking
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*Richard McElreath*
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^^^^^^^^
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A Bayesian Course with Examples in R and Stan.
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:::{list-table}
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:header-rows: 0
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:widths: 4 60
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* - [{fa}`link`][rethinking website]
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- [Book Website][rethinking website]
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* - [{fa}`link`][rethinking port]
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- [PyMC port of the code][rethinking port]
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:::
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---
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:img-top: https://raw.githubusercontent.com/aloctavodia/BAP/master/Cover.png
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Bayesian Analysis with Python
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*Osvaldo Martin*
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^^^^^^^^
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A great introductory book written by a maintainer of PyMC.
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:::{list-table}
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:header-rows: 0
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:widths: 4 60
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[Discourse](https://discourse.pymc.io/)
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* - [{fa}`link`][bap website]
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- [Book Website][bap website]
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* - [{fa}`link`][bap code]
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- [Code and errata in PyMC][bap code]
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:::
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---
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:img-top: https://images-na.ssl-images-amazon.com/images/I/51K33XI2I8L._SX330_BO1,204,203,200_.jpg
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Bayesian Cognitive Modeling: A Practical Course
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*Michael Lee and Eric-Jan Wagenmakers*
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^^^^^^^^
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Focused on using Bayesian statistics in cognitive modeling.
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:::{list-table}
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:header-rows: 0
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:widths: 4 60
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* - [{fa}`link`][bcm website]
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- [Book Website][bcm website]
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* - [{fa}`link`][bcm code]
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- [PyMC implementation][bcm code]
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:::
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---
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:img-top: http://www.stat.columbia.edu/~gelman/book/bda_cover.png
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Bayesian Data Analysis
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*Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin*
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^^^^^^^^
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A comprehensive, standard, and wonderful textbook on Bayesian methods.
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:::{list-table}
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:header-rows: 0
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:widths: 4 60
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* - [{fa}`link`][bda3 website]
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- [Book Website][bda3 website]
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* - [{fa}`link`][bda3 port]
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- [Examples and exercises implemented in PyMC][bda3 port]
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[hackers source]: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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[hackers homepage]: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
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[dbda website]: https://sites.google.com/site/doingbayesiandataanalysis/home
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[dbda 1st ed nb]: https://github.com/aloctavodia/Doing_bayesian_data_analysis
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[dbda 2nd ed nb]: https://github.com/JWarmenhoven/DBDA-python
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[rethinking website]: http://xcelab.net/rm/statistical-rethinking/
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[rethinking port]: https://github.com/pymc-devs/resources/tree/master/Rethinking
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[bap website]: https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition
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[bap code]: https://github.com/aloctavodia/BAP
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[bcm website]: https://bayesmodels.com/
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[bcm code]: https://github.com/pymc-devs/resources/tree/master/BCM
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[bda3 website]: https://www.stat.columbia.edu/~gelman/book/
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[bda3 port]: https://github.com/pymc-devs/resources/tree/master/BDA3

docs/source/conf.py

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docs/source/contributing/index.md

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New to the open source space? Find a good beginner friendly documentation issue you can help with [here](https://github.com/pymc-devs/pymc/issues?q=is%3Aissue+is%3Aopen+label%3A%22beginner+friendly%22+label%3A%22docs%22) or [here](https://github.com/pymc-devs/pymc-examples/issues?q=is%3Aopen+label%3Adocs+label%3A%22good+first+issue%22).
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New to the open source space? Find a good beginner friendly documentation issue you can help with [here](https://github.com/pymc-devs/pymc/issues?q=is%3Aissue+is%3Aopen+label%3A%22beginner+friendly%22+label%3A%22docs%22) or [here](https://github.com/pymc-devs/pymc-examples/issues?q=is%3Aopen+label%3Adocs+label%3A%22good+first+issue%22).
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You will need to express your interest in the issue by commenting on it using your Github account, and you will need to use some beginner level Git to complete the Pull Request process, but don't worry! There are plenty of good [tutorials](https://guides.github.com/activities/hello-world/) out there, and PyMC reviewers are happy to help.
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docs/source/index.md

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