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pymc 2.1beta
Markov Chain Monte Carlo sampling toolkit.
Latest Version: 2.2
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.
| File | Type | Py Version | Uploaded on | Size | # downloads |
|---|---|---|---|---|---|
| pymc-2.1beta-py2.6-macosx-10.6-universal.egg (md5) | Python Egg | 2.6 | 2010-03-23 | 1MB | 938 |
| pymc-2.1beta.win32-py2.5.exe (md5) | MS Windows installer | 2.5 | 2010-03-22 | 964KB | 372 |
| pymc-2.1beta.win32-py2.6.exe (md5) | MS Windows installer | 2.6 | 2010-03-22 | 976KB | 935 |
| pymc-2.1beta.zip (md5) | Source | 2010-03-22 | 1MB | 5115 | |
- Author: Christopher Fonnesbeck, Anand Patil and David Huard
- Home Page: pymc.googlecode.com
- License: Academic Free License
- Requires NumPy (>=1.3)
- Categories
- Package Index Owner: davidhuard, fonnesbeck, anandpatil
- Package Index Maintainer: fonnesbeck, davidhuard
- DOAP record: pymc-2.1beta.xml