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Getting docs ready for v4
example doc restructure working and hopefully illustrative doc skeleton update requirements WIP Change log: - Upload new PyMC logo - Updated conf.py to pymc instead of pymc3 - Start configuring and populating index page - Start configuring and populating learning page - Start configuring and populating api page - Updated API to follow the docs/Architecture.png layout - Start configuring and populating learning page - Fix typo in filename of "index legacy" add community page, other minor changes Continue restructuring the API Delete conflicting files example doc restructure update requirements update requirements and gitignore some fixes add changes introduced by pre-commit checks fix community page title update api for distributions update gaussian process api fix end of file more changes sponsor logos sponsor logos cleanup, other changes changes to satisfy pre-commit checks requirements changes minor changes Add testimonials to docs homepage remove sphinx panels and other changes center buttons add explanation of MAP to glassor minor change fix typo minor changes put content in videos and podcasts section. Change formatting of testimonials (width of 2 cards for better formating in narrow screens) add code example to front page revise some definitions (#5126) minor changes minor changes WIP: organizing notebooks into learning section
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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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‎.gitmodules

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‎conda-envs/environment-dev-py37.yml

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‎conda-envs/environment-dev-py38.yml

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‎conda-envs/environment-dev-py39.yml

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‎conda-envs/windows-environment-dev-py38.yml

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‎docs/source/_templates/layout.html

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‎docs/source/about.rst

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_href from docs/source/index.rst
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**********
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* Bayesian models can be embedded in larger programs, and results can be analyzed
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with the full power of Python.
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What's new in version 4
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=======================
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:bdg-warning:`TODO`
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What's new in version 3
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=======================
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Move this section to a different place
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The third major version of PyMC has benefitted from being re-written from scratch. Substantial improvements in the user interface and performance have resulted from this. While PyMC2 relied on Fortran extensions (via f2py) for most of the computational heavy-lifting, PyMC leverages Aesara, a fork of the Theano library from the Montréal Institute for Learning Algorithms (MILA), for array-based expression evaluation, to perform its computation. What this provides, above all else, is fast automatic differentiation, which is at the heart of the gradient-based sampling and optimization methods currently providing inference for probabilistic programming.
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‎docs/source/api.rst

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api/glm
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api/plots
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api/stats
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api/data
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api/variables
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api/smc
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Indices and tables
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===================

‎docs/source/api/backends.rst

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‎docs/source/api/distributions.rst

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distributions/continuous
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distributions/logprob
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‎docs/source/api/distributions/continuous.rst

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Normal
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HalfNormal
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HalfNormal
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‎docs/source/api/distributions/discrete.rst

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‎docs/source/api/distributions/simulator.rst

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‎docs/source/api/distributions/transforms.rst

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