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!
- Intuitive model specification syntax, for example,
x ~ N(0,1)translates tox = 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
- PyMC 3 Tutorial
- Coal Mining Disasters model in PyMC 2 and PyMC 3
- Stochastic Volatility model guided example
pip install git+https://github.com/pymc-devs/pymc
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