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PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning
focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI)
algorithms. Its flexibility and extensibility make it applicable to a
large suite of problems.
Check out the
getting started guide <http://docs.pymc.io/notebooks/getting_started>, or
interact with live examples <https://mybinder.org/v2/gh/pymc-devs/pymc3/master?filepath=%2Fdocs%2Fsource%2Fnotebooks>
- Intuitive model specification syntax, for example,
x ~ N(0,1)
x = Normal('x',0,1)
- Powerful sampling algorithms, such as the
No U-Turn Sampler <http://www.jmlr.org/papers/v15/hoffman14a.html>__, allow complex models
with thousands of parameters with little specialized knowledge of
- Variational inference:
for fast approximate posterior estimation as well as mini-batch ADVI
for large data sets.
- Relies on
Theano <http://deeplearning.net/software/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
If you already know about Bayesian statistics:
API quickstart guide <http://docs.pymc.io/notebooks/api_quickstart>__
PyMC3 tutorial <http://docs.pymc.io/notebooks/getting_started>__
PyMC3 examples <http://docs.pymc.io/examples>and the
API reference <http://docs.pymc.io/api>
Learn Bayesian statistics with a book together with PyMC3:
Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>__: Fantastic book with many applied code examples.
PyMC3 port of the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/aloctavodia/Doing_bayesian_data_analysis>as well as the
second edition <https://github.com/JWarmenhoven/DBDA-python>: Principled introduction to Bayesian data analysis.
PyMC3 port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath <https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3>__
PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3>__: Focused on using Bayesian statistics in cognitive modeling.
Bayesian Analysis with Python by Osvaldo Martin <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python>(and
errata <https://github.com/aloctavodia/BAP>): Great introductory book.
There are also several talks on PyMC3 which are gathered in this
YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>__
The latest release of PyMC3 can be installed from PyPI using
pip install pymc3
pip install pymc will install PyMC 2.3, not PyMC3,
Or via conda-forge:
conda install -c conda-forge pymc3
The current development branch of PyMC3 can be installed from GitHub, also using
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
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.
PyMC3 is tested on Python 2.7 and 3.6 and depends on Theano, NumPy,
SciPy, Pandas, and Matplotlib (see
requirements.txt for version
In addtion to the above dependencies, the GLM submodule relies on
Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming
in Python using PyMC3. PeerJ Computer Science 2:e55
DOI: 10.7717/peerj-cs.55 <https://doi.org/10.7717/peerj-cs.55>__.
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.
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>.
To report an issue with PyMC3 please use the
issue tracker <https://github.com/pymc-devs/pymc3/issues>__.
Finally, if you need to get in touch for non-technical information about the project,
send us an e-mail <email@example.com>__.
Apache License, Version
Software using PyMC3
Bambi <https://github.com/bambinos/bambi>__: BAyesian Model-Building Interface (BAMBI) in Python.
pymc3_models <https://github.com/parsing-science/pymc3_models>__: Custom PyMC3 models built on top of the scikit-learn API.
webmc3 <https://github.com/AustinRochford/webmc3>__: A web interface for exploring PyMC3 traces
sampled <https://github.com/ColCarroll/sampled>__: Decorator for PyMC3 models.
NiPyMC <https://github.com/PsychoinformaticsLab/nipymc>__: Bayesian mixed-effects modeling of fMRI data in Python.
beat <https://github.com/hvasbath/beat>__: Bayesian Earthquake Analysis Tool.
Please contact us if your software is not listed here.
Papers citing PyMC3
Google Scholar <https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011>__ for a continuously updated list.
PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate
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