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.