Skip to main content

A framework for approximate Bayesian computation (ABC) that speeds up inference by parallelizing computation on single computers or whole clusters.

Project description

ABCpy is a highly modular, scientific library for approximate Bayesian computation (ABC) written in Python. It is designed to run all included ABC algorithms in parallel, either using multiple cores of a single computer or using an Apache Spark or MPI enabled cluster. The modularity helps domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization, even without much additional effort to parallelize their code. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment, and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy.

Project details

Download files

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

Source Distribution

abcpy-0.6.3.tar.gz (165.4 kB view hashes)

Uploaded source

Built Distribution

abcpy-0.6.3-py3-none-any.whl (183.8 kB view hashes)

Uploaded py3

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