ABCpy is a scientific library for approaximate Bayesian computation (ABC) written in Python. It addresses the needs of domain scientists and data scientists by providing
- a fully modularized framework that is easy to use and easy to extend, and
- a non-intrusive, user-friendly way to parallelize inference computations
- Quickly infer parameters for already existing models
- Quickly integrate your model into the framework
- Easily parallelize the inferrence computation when models become complex
ABCpy is published under the BSD 3-clause license, see here.
You are very welcome to contribute to ABCpy.
If you want to contribute code, there are a few things to consider:
- a good start is to fork the repository
- use GitHub pull requests to merge your contribution
- consider documenting your code according to the NumPy documentation style guide
- consider writing reasonable unit tests
In case of any questions, feel free to contact one of us:
- Ritabrata Dutta, University of Lugano
- Marcel Schoengens, CSCS, ETH Zurich
