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.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|pymc-2.3.6.py27-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 hash SHA256||Egg||2.7|
|pymc-2.3.6.py34-macosx-x86_64.tar.gz (1.3 MB) Copy SHA256 hash SHA256||Egg||3.4|
|pymc-2.3.6.py35-macosx-x86_64.tar.gz (1.1 MB) Copy SHA256 hash SHA256||Egg||3.5|
|pymc-2.3.6.tar.gz (348.4 kB) Copy SHA256 hash SHA256||Source||None|
|pymc-2.3.6.zip (402.8 kB) Copy SHA256 hash SHA256||Source||None|