Skip to main content

A toolkit for adaptive importance sampling featuring implementations of variational Bayes, population Monte Carlo, and Markov chains.

Project description

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. Importance sampling supports parallelization on multiple machines via mpi4py.

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)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release. See tutorial on generating distribution archives.

Built Distributions

pypmc-1.2.3-cp311-cp311-macosx_11_0_arm64.whl (804.9 kB view hashes)

Uploaded cp311

pypmc-1.2.3-cp311-cp311-macosx_10_9_x86_64.whl (873.3 kB view hashes)

Uploaded cp311

pypmc-1.2.3-cp310-cp310-macosx_11_0_arm64.whl (802.9 kB view hashes)

Uploaded cp310

pypmc-1.2.3-cp310-cp310-macosx_10_9_x86_64.whl (871.0 kB view hashes)

Uploaded cp310

pypmc-1.2.3-cp39-cp39-macosx_11_0_arm64.whl (805.1 kB view hashes)

Uploaded cp39

pypmc-1.2.3-cp39-cp39-macosx_10_9_x86_64.whl (873.3 kB view hashes)

Uploaded cp39

pypmc-1.2.3-cp38-cp38-macosx_11_0_arm64.whl (806.2 kB view hashes)

Uploaded cp38

pypmc-1.2.3-cp38-cp38-macosx_10_9_x86_64.whl (873.6 kB view hashes)

Uploaded cp38

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page