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Primitives for Bayesian MCMC inference

Project description

# Distributions

[![Build Status](https://travis-ci.org/posterior/distributions.svg?branch=master)](https://travis-ci.org/posterior/distributions) [![Latest Version](https://badge.fury.io/py/distributions.svg)](https://pypi.python.org/pypi/distributions) [![DOI](https://zenodo.org/badge/29212688.svg)](https://zenodo.org/badge/latestdoi/29212688)

Distributions provides low-level primitives for collapsed Gibbs sampling 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.

## Installation

For python-only support (no C++) you can install with pip:

pip install distributions

For help with other builds, see [the installation documentation](http://distributions.readthedocs.org/en/latest/installation.html).

## Documentation

The official documentation lives at http://distributions.readthedocs.org/.

Branch-specific documentation lives at

  • [Overview](/doc/overview.rst)
  • [Installation](/doc/installation.rst)

## Authors (alphabetically)

## License

Copyright (c) 2014 Salesforce.com, Inc. All rights reserved.

Licensed under the Revised BSD License. See [LICENSE.txt](LICENSE.txt) for details.

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