hongseok-yang / pymc

NOTICE: master branch now contains PyMC 3 code. PyMC 2 is in branch 2.3.

PyMC 3

PyMC 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 Tutorial!

Features

• Intuitive model specification syntax, for example, x ~ N(0,1) translates to x = Normal(0,1)
• Powerful sampling algorithms such as Hamiltonian Monte Carlo
• Easy optimization for finding the maximum a posteriori point
• Theano features
• Numpy broadcasting and advanced indexing
• Linear algebra operators
• Computation optimization and dynamic C compilation
• Simple extensibility

Getting started

• PyMC 3 Tutorial
• Coal Mining Disasters model in PyMC 2 and PyMC 3
• Global Health Metrics & Evaluation model case study for GHME 2013
• Stochastic Volatility model

Installation

pip install git+https://github.com/pymc-devs/pymc

Optional

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

License

Apache License, Version 2.0