Welcome to PyMC3’s documentation!

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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!


  • 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


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.


PyMC3 is tested on Python 2.7 and 3.6 and depends on Theano, NumPy, SciPy, Pandas, and Matplotlib (see requirements.txt for version information).


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


To report an issue with PyMC3 or to suggest a feature please use the issue tracker.

To ask a question regarding modeling or usage of PyMC3 we encourage posting to StackOverflow using the “pymc” tag.

To interact with PyMC3 developers, visit the pymc Gitter channel.

Finally, if you need to get in touch for non-technical information about the project, send us an e-mail.

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.


See the GitHub contributor page





Indices and tables