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.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|pymc-2.3.6.py27-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 Checksum SHA256||2.7||Egg||Oct 16, 2015|
|pymc-2.3.6.py34-macosx-x86_64.tar.gz (1.3 MB) Copy SHA256 Checksum SHA256||3.4||Egg||Oct 16, 2015|
|pymc-2.3.6.py35-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 Checksum SHA256||3.5||Egg||Nov 5, 2015|
|pymc-2.3.6.tar.gz (348.4 kB) Copy SHA256 Checksum SHA256||–||Source||Oct 16, 2015|
|pymc-2.3.6.zip (402.8 kB) Copy SHA256 Checksum SHA256||–||Source||Oct 16, 2015|