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Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
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PyMC3 logo

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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, or interact with live examples using Binder!


  • 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

If you already know about Bayesian statistics:

Learn Bayesian statistics with a book together with PyMC3:

PyMC3 talks

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:

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

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:

pip install git+

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
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 install or python develop.


PyMC3 is tested on Python 3.6 and depends on Theano, NumPy, SciPy, and Pandas (see requirements.txt for version information).


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

Citing PyMC3

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.


We are using as our main communication channel. You can also follow us on Twitter @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. You can also suggest feature in the “Development” Category.

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.


Apache License, Version 2.0

Software using PyMC3

  • Exoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
  • Bambi: BAyesian Model-Building Interface (BAMBI) in Python.
  • pymc3_models: Custom PyMC3 models built on top of the scikit-learn API.
  • PMProphet: PyMC3 port of Facebook's Prophet model for timeseries modeling
  • webmc3: A web interface for exploring PyMC3 traces
  • sampled: Decorator for PyMC3 models.
  • NiPyMC: Bayesian mixed-effects modeling of fMRI data in Python.
  • beat: Bayesian Earthquake Analysis Tool.

Please contact us if your software is not listed here.

Papers citing PyMC3

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.





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