Skip to main content

Markov Chain Monte Carlo sampling toolkit.

Project description

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.

Project details


Release history Release notifications

History Node

2.3.6

History Node

2.3.5

History Node

2.3.4

History Node

2.3.3

History Node

2.3.2

History Node

2.3.1

History Node

2.3

This version
History Node

2.2

History Node

2.1beta

History Node

2.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

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

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page