Primitives for Bayesian MCMC inference
# Distributions [![Build Status](https://travis-ci.org/forcedotcom/distributions.svg?branch=master)](https://travis-ci.org/forcedotcom/distributions)
Distributions provides low-level primitives for Bayesian MCMC inference in Python and C++ including:
- special numerical functions,
- samplers and density functions from a variety of distributions,
- conjugate component models (e.g., gamma-Poisson, normal-inverse-chi-squared),
- clustering models (e.g., CRP, Pitman-Yor), and
- efficient wrappers for mixture models.
Distributions powered a machine-learning-as-a-service for Prior Knowledge Inc., and now powers machine learning infrastructure at Salesforce.com.
distributions with pip:
pip install distributions
For help with other builds, see [the installation documentation](http://distributions.readthedocs.org/en/latest/installation.html).
The official documentation lives at http://distributions.readthedocs.org/.
Branch-specific documentation lives at
## Authors (alphabetically)
- Jonathan Glidden <https://twitter.com/jhglidden>
- Eric Jonas <https://twitter.com/stochastician>
- Fritz Obermeyer <https://github.com/fritzo>
- Cap Petschulat <https://github.com/cap>
Copyright (c) 2014 Salesforce.com, Inc. All rights reserved.
Licensed under the Revised BSD License. See [LICENSE.txt](LICENSE.txt) for details.