Markov Chain Monte Carlo sampling toolkit.
- 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.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|pymc-2.2-py2.6-linux-x86_64.egg (1.2 MB) Copy SHA256 hash SHA256||Egg||2.6||May 7, 2012|
|pymc-2.2-py2.7-macosx-10.7-intel.egg (1.0 MB) Copy SHA256 hash SHA256||Egg||2.7||May 7, 2012|
|pymc-2.2-py2.7-win32.egg (1.2 MB) Copy SHA256 hash SHA256||Egg||2.7||May 7, 2012|
|pymc-2.2.tar.gz (351.8 kB) Copy SHA256 hash SHA256||Source||None||May 8, 2012|
|pymc-2.2.win32-py2.7.exe (1.1 MB) Copy SHA256 hash SHA256||Windows Installer||2.7||Jul 12, 2012|