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.
- 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
- Tutorial model
- More advanced Stochastic Volatility model
pip install git+https://github.com/pymc-devs/pymc@pymc3
scikits.sparse enables sparse scaling matrices which are useful for large problems.
Ubuntu:
sudo apt-get install libsuitesparse-dev
pip install git+https://github.com/njsmith/scikits-sparse.git