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.
x ~ N(0,1)translates to
x = Normal('x',0,1)
There are also several talks on PyMC3 which are gathered in this YouTube playlist
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
Plotting is done using ArviZ which may be installed separately, or along with PyMC3:
pip install pymc3[plots]
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 3.6 and depends on Theano, NumPy,
SciPy, and Pandas (see
requirements.txt for version
In addtion to the above dependencies, the GLM submodule relies on Patsy.
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.
To report an issue with PyMC3 please use the issue tracker.
Finally, if you need to get in touch for non-technical information about the project, send us an e-mail.
Please contact us if your software is not listed here.
See Google Scholar for a continuously updated list.
See the GitHub contributor page
PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate here.