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

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PyMC 3

Build Status

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

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

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

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