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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.

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
pymc-2.3.6.py27-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 hash SHA256 Egg 2.7
pymc-2.3.6.py34-macosx-x86_64.tar.gz (1.3 MB) Copy SHA256 hash SHA256 Egg 3.4
pymc-2.3.6.py35-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 hash SHA256 Egg 3.5
pymc-2.3.6.tar.gz (348.4 kB) Copy SHA256 hash SHA256 Source None (402.8 kB) Copy SHA256 hash SHA256 Source None

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