pypmc is a python package focusing on adaptive importance
sampling. It can be used for integration and sampling from a
user-defined target density. A typical application is Bayesian
inference, where one wants to sample from the posterior to marginalize
over parameters and to compute the evidence. The key idea is to create
a good proposal density by adapting a mixture of Gaussian or student's
t components to the target density. The package is able to efficiently
integrate multimodal functions in up to about 30-40 dimensions at the
level of 1% accuracy or less. For many problems, this is achieved
without requiring any manual input from the user about details of the
function. pypmc supports importance sampling on a cluster of
machines via mpi4py out of the box.
Useful tools that can be used stand-alone include:
- importance sampling (sampling & integration)
- adaptive Markov chain Monte Carlo (sampling)
- variational Bayes (clustering)
- population Monte Carlo (clustering)
Instructions are maintained here.
Fully documented examples are shipped in the examples subdirectory
of the source distribution or available online including sample
output
here. Feel
free to save and modify them according to your needs.
The full documentation with a manual and api description is available at here.
pypmc was developed by Stephan Jahn (TU Munich) under the supervision of Frederik Beaujean (LMU Munich) as part of Stephan's master's thesis at the Excellence Cluster Universe, Garching, Germany, in 2014.
If you use pypmc in academic work, we kindly ask you to cite the
respective release as indicated by the zenodo DOI above. Thanks!
Day to day maintenance is assisted by Danny van Dyk.