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pymc 2.1beta
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.
| 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 | 731 |
| pymc-2.1beta.win32-py2.5.exe (md5) | MS Windows installer | 2.5 | 2010-03-22 | 964KB | 198 |
| pymc-2.1beta.win32-py2.6.exe (md5) | MS Windows installer | 2.6 | 2010-03-22 | 976KB | 690 |
| pymc-2.1beta.zip (md5) | Source | 2010-03-22 | 1MB | 4022 | |
- 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: davidhuard
- DOAP record: pymc-2.1beta.xml