Markov Chain Monte Carlo sampling toolkit.
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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|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|