swMATH ID: 29748
Software Authors: Dutta, R., Schoengens, M., Ummadisingu, A., Widmer, N., Onnela, J.-P., and Mira, A.
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.
Homepage: https://pypi.org/project/abcpy/
Source Code:  https://github.com/eth-cscs/abcpy
Dependencies: Python
Keywords: Python; Journal of Statistical Software; ABCpy; approximate Bayesian computation; ABC; HPC; Spark; MPI; parallel; imbalance; Python library
Related Software: PyTorch; EasyABC; pyABC; ELFI; abc; al3c; astroABC; ipyparallel; Dask; IPython; SWIG; SciPy; NumPy; Apache Spark; OpenMPI; ABC-SysBio; Python; Joblib; DELFI; hypothesis
Cited in: 2 Publications

Standard Articles

1 Publication describing the Software Year

Citations by Year