https://github.com/architecture-building-systems/pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

https://github.com/architecture-building-systems/pymc3

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Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

README.rst

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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.

Check out the `getting started
guide `__!

Features
--------

-  Intuitive model specification syntax, for example, ``x ~ N(0,1)``
   translates to ``x = Normal('x',0,1)``
-  **Powerful sampling algorithms**, such as the `No U-Turn
   Sampler `__, allow complex models
   with thousands of parameters with little specialized knowledge of
   fitting algorithms.
-  **Variational inference**: `ADVI `__
   for fast approximate posterior estimation as well as mini-batch ADVI
   for large data sets.
-  Relies on `Theano `__ which provides:
    *  Computation optimization and dynamic C compilation
    *  Numpy broadcasting and advanced indexing
    *  Linear algebra operators
    *  Simple extensibility
-  Transparent support for missing value imputation

Getting started
---------------

-  The `PyMC3 tutorial `__ 
-  `PyMC3 examples `__
   and the `API reference `__
-  `Probabilistic Programming and Bayesian Methods for Hackers `__
-  `Bayesian Modelling in Python -- tutorials on Bayesian statistics and
   PyMC3 as Jupyter Notebooks by Mark
   Dregan `__
-  `Talk at PyData London 2016 on
   PyMC3 `__
-  `PyMC3 port of the models presented in the book "Doing Bayesian Data
   Analysis" by John
   Kruschke `__
-  `Coyle P. (2016) Probabilistic programming and PyMC3. European Scientific Python Conference 2015 (Cambridge, UK) `__
-  `Bayesian Analysis with Python by Osvaldo Martin `__

Installation
------------

The latest release of PyMC3 can be installed from PyPI using ``pip``:

::

    pip install pymc3

**Note:** Running ``pip install pymc`` will install PyMC 2.3, not PyMC3,
from PyPI.

Or via conda-forge:

::

    conda install -c conda-forge pymc3

The current development branch of PyMC3 can be installed from GitHub, also using ``pip``:

::

    pip install git+https://github.com/pymc-devs/pymc3

To ensure the development branch of Theano is installed alongside PyMC3
(recommended), you can install PyMC3 using the ``requirements.txt``
file. This requires cloning the repository to your computer:

::

    git clone https://github.com/pymc-devs/pymc3
    cd pymc3
    pip install -r requirements.txt

However, if a recent version of Theano has already been installed on
your system, you can install PyMC3 directly from GitHub.

Another option is to clone the repository and install PyMC3 using
``python setup.py install`` or ``python setup.py develop``.


Dependencies
------------

PyMC3 is tested on Python 2.7 and 3.5 and depends on Theano, NumPy,
SciPy, Pandas, and Matplotlib (see ``requirements.txt`` for version
information).

Optional
~~~~~~~~

In addtion to the above dependencies, the GLM submodule relies on
`Patsy `__.

`scikits.sparse `__
enables sparse scaling matrices which are useful for large problems.

Citing PyMC3
------------

Salvatier J, Wiecki TV, Fonnesbeck C. (2016) Probabilistic programming
in Python using PyMC3. PeerJ Computer Science 2:e55
https://doi.org/10.7717/peerj-cs.55

License
-------

`Apache License, Version
2.0 `__


Software using PyMC3
--------------------

 - `Bambi `__: BAyesian Model-Building Interface (BAMBI) in Python.
 - `NiPyMC `__: Bayesian mixed-effects modeling of fMRI data in Python.
 - `gelato `__: Bayesian Neural Networks with PyMC3 and Lasagne.
 - `beat `__: Bayesian Earthquake Analysis Tool.
 - `Edward `__: A library for probabilistic modeling, inference, and criticism.

Please contact us if your software is not listed here.

Papers citing PyMC3
-------------------

See `Google Scholar `__ for a continuously updated list.

Contributors
------------

See the `GitHub contributor
page `__

Sponsors
--------

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|Quantopian|

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