Probabilistic Programming in Python
Project description
PyMC3 is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
Check out the getting started guide!
PyMC3 is beta software. Users should consider using PyMC 2 repository.
Features
Intuitive model specification syntax, for example, x ~ N(0,1) translates to x = Normal(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.
Easy optimization for finding the maximum a posteriori (MAP) point
Theano features
Numpy broadcasting and advanced indexing
Linear algebra operators
Computation optimization and dynamic C compilation
Simple extensibility
Transparent support for missing value imputation
Getting started
Installation
The latest version of PyMC3 can be installed from the master branch 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.
Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.
Dependencies
PyMC3 is tested on Python 2.7 and 3.3 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[http://patsy.readthedocs.io/en/latest/].
`scikits.sparse <https://github.com/njsmith/scikits-sparse>`__ enables sparse scaling matrices which are useful for large problems. Installation on Ubuntu is easy:
sudo apt-get install libsuitesparse-dev pip install git+https://github.com/njsmith/scikits-sparse.git
On Mac OS X you can install libsuitesparse 4.2.1 via homebrew (see http://brew.sh/ to install homebrew), manually add a link so the include files are where scikits-sparse expects them, and then install scikits-sparse:
brew install suite-sparse ln -s /usr/local/Cellar/suite-sparse/4.2.1/include/ /usr/local/include/suitesparse pip install git+https://github.com/njsmith/scikits-sparse.git
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
Coyle P. (2016) Probabilistic programming and PyMC3. European Scientific Python Conference 2015 (Cambridge, UK) http://adsabs.harvard.edu/abs/2016arXiv160700379C
License
Contributors
See the GitHub contributor page
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