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Project Description

Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. However, few statistical software packages implement MCMC samplers, and they are non-trivial to code by hand. pymc3 is a python package that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems. pymc3 includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

Release History

Release History

3.0rc4

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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3.0rc2

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TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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3.0.rc1

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TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pymc3-3.0rc4-py3-none-any.whl (788.8 kB) Copy SHA256 Checksum SHA256 3.5 Wheel Dec 1, 2016
pymc3-3.0rc4.tar.gz (13.0 MB) Copy SHA256 Checksum SHA256 Source Dec 1, 2016

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