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Primitives for Bayesian MCMC inference

Project description

# Distributions

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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

## Installation

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

pip install distributions

For help with other builds, see [the installation documentation](

## Documentation

The official documentation lives at

Branch-specific documentation lives at

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

## Authors (alphabetically)

## License

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

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

Project details

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Files for distributions, version 2.2.1
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distributions-2.2.1.tar.gz (1.5 MB) View hashes Source None

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