Metadata-Version: 2.1
Name: abcpy
Version: 0.5.1
Summary: A framework for approximate Bayesian computation (ABC) that speeds up inference by parallelizing computation on single computers or whole clusters.
Home-page: https://github.com/eth-cscs/abcpy
Author: The abcpy authors
Author-email: UNKNOWN
License: BSD-3
Download-URL: https://github.com/eth-cscs/abcpy/archive/v0.5.1.tar.gz
Keywords: abcpy
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: numpy (==1.14)
Requires-Dist: scipy
Requires-Dist: sklearn
Requires-Dist: glmnet (==2.0.0)
Requires-Dist: sphinx (==1.4.8)
Requires-Dist: sphinx-rtd-theme
Requires-Dist: coverage

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.


