pymc 2.0.beta

Markov Chain Monte Carlo sampling toolkit.

Latest Version: 2.0

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.

• Author: David Huard <david huard at gmail com>
• Maintainer: David Huard <david huard at gmail com>
• Home Page: pymc.googlecode.com
• Keywords: Bayesian, statistics, MCMC, stochastic simulation
• License: Academic Free License
• Requires matplotlib, numpy
• Categories
  • Development Status :: 4 - Beta
  • Environment :: Console
  • Intended Audience :: Science/Research
  • License :: OSI Approved :: Academic Free License (AFL)
  • Operating System :: OS Independent
  • Programming Language :: Fortran
  • Programming Language :: Python
  • Topic :: Scientific/Engineering
• Package Index Owner: davidhuard, fonnesbeck, anandpatil
• Package Index Maintainer: davidhuard
• DOAP record: pymc-2.0.beta.xml