<commit_before># PyMC 3

PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems.

## Guided Examples
 * [Simple model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/tutorial.ipynb)
 * More advanced [Stochastic Volatility model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/stochastic_volatility.ipynb)

# Features 
 * Advanced sampling algorithms like Hamiltonian Monte Carlo
 * [Theano](http://deeplearning.net/software/theano/) based optimization and dynamic C code generation.
<commit_msg>DOC: Added build status icon to readme.md.<commit_after># PyMC 3

[![Build Status](https://travis-ci.org/pymc-devs/pymc.png)](https://travis-ci.org/pymc-devs/pymc)

PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems.

## Guided Examples
 * [Simple model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/tutorial.ipynb)
 * More advanced [Stochastic Volatility model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/stochastic_volatility.ipynb)

# Features 
 * Advanced sampling algorithms like Hamiltonian Monte Carlo
 * [Theano](http://deeplearning.net/software/theano/) based optimization and dynamic C code generation.

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