BayesPowerlaw

Written by Kristina Grigaityte.

_images/tweet_powerlaw.png _images/tweet_posterior.png

BayesPowerlaw is a Python package that fits a single or a mixture of power law distributions to data using a Bayesian inference approach. Posterior distributions of parameters are numerically determined by Markov chain Monte Carlo sampling. In addition, the package provides capability for - power law simulations - data plotting - maximum likelihood estimation

Installation

BayesPowerlaw can be installed from PyPI using the pip package manager (version 9.0.0 or higher). At the command line:

pip install BayesPowerlaw

The code for BayesPowerlaw is open source and available on GitHub.

Quick Start

To make the figures shown above, type this from within Python:

import BayesPowerlaw as bp
bp.demo()

Contact

For technical assistance or to report bugs, please contact Kristina Grigaityte.

For general correspondence, please contact Gurinder (Mickey) Atwal.

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