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Markov Chain Monte Carlo sampling toolkit.

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. pymc 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. pymc includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

pymc only requires NumPy. All other dependencies such as matplotlib, SciPy, pytables, sqlite or mysql are optional.

Release History

Release History

This version
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2.3.6

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2.3.5

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2.3.4

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2.3.3

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2.3.2

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2.3.1

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2.3

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2.2

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2.1beta

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2.0

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pymc-2.3.6.py27-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 Checksum SHA256 2.7 Egg Oct 16, 2015
pymc-2.3.6.py34-macosx-x86_64.tar.gz (1.3 MB) Copy SHA256 Checksum SHA256 3.4 Egg Oct 16, 2015
pymc-2.3.6.py35-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 Checksum SHA256 3.5 Egg Nov 5, 2015
pymc-2.3.6.tar.gz (348.4 kB) Copy SHA256 Checksum SHA256 Source Oct 16, 2015
pymc-2.3.6.zip (402.8 kB) Copy SHA256 Checksum SHA256 Source Oct 16, 2015

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