gituser/production/: pymc-2.3.7 metadata and description

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Markov Chain Monte Carlo sampling toolkit.

author Christopher Fonnesbeck, Anand Patil and David Huard
author_email fonnesbeck@gmail.com
classifiers
  • Development Status :: 5 - Production/Stable
  • Environment :: Console
  • Operating System :: OS Independent
  • Intended Audience :: Science/Research
  • License :: OSI Approved :: Academic Free License (AFL)
  • Programming Language :: Python
  • Programming Language :: Fortran
  • Topic :: Scientific/Engineering
license Academic Free License
requires
  • NumPy (>=1.8)
File Tox results History
pymc-2.3.7-cp27-cp27mu-linux_x86_64.whl
Size
1 MB
Type
Python Wheel
Python
2.7
pymc-2.3.7-cp37-cp37m-linux_x86_64.whl
Size
1 MB
Type
Python Wheel
Python
3.7

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.