ABCpy is a scientific library written in Python for Bayesian uncertainty quantification in absence of likelihood function, which parallelizes existing approximate Bayesian computation (ABC) algorithms and other likelihood-free inference schemes. It presently includes:
- RejectionABC
- PMCABC (Population Monte Carlo ABC)
- SMCABC (Sequential Monte Carlo ABC)
- RSMCABC (Replenishment SMC-ABC)
- APMCABC (Adaptive Population Monte Carlo ABC)
- SABC (Simulated Annealing ABC)
- ABCsubsim (ABC using subset simulation)
- PMC (Population Monte Carlo) using approximations of likelihood functions
- Random Forest Model Selection Scheme
- Semi-automatic summary selection
ABCpy addresses the needs of domain scientists and data scientists by providing
- a fully modularized framework that is easy to use and easy to extend,
- a quick way to integrate your generative model into the framework (from C++, R etc.) and
- a non-intrusive, user-friendly way to parallelize inference computations (for your laptop to clusters, supercomputers and AWS)
- an intuitive way to perform inference on hierarchical models or more generally on Bayesian networks
For more information, check out the
- Documentation
- Examples directory and
- Reference
Further, we provide a collection of models for which ABCpy has been applied successfully. This is a good place to look at more complicated inference setups.
ABCpy was written by Ritabrata Dutta, Warwick University and Marcel Schoengens, CSCS, ETH Zurich, and we're actively developing it. Please feel free to submit any bugs or feature requests. We'd also love to hear about your experiences with ABCpy in general. Drop us an email!
We want to thank Prof. Antonietta Mira, Università della svizzera italiana, and Prof. Jukka-Pekka Onnela, Harvard University for helpful contributions and advice; Avinash Ummadisinghu and Nicole Widmern respectively for developing dynamic-MPI backend and making ABCpy suitable for hierarchical models; and finally CSCS (Swiss National Super Computing Center) for their generous support.
There is a paper in the proceedings of the 2017 PASC conference. In case you use ABCpy for your publication, we would appreciate a citation. You can use this
BibTex reference.
Publications in which ABCpy was applied:
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R. Dutta, J. P. Onnela, A. Mira, "Bayesian Inference of Spreading Processes on Networks", 2018, Proc. R. Soc. A, 474(2215), 20180129.
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R. Dutta, Z. Faidon Brotzakis and A. Mira, "Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water", 2018, Journal of Chemical Physics 149, 154110.
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R. Dutta, B. Chopard, J. Lätt, F. Dubois, K. Zouaoui Boudjeltia and A. Mira, "Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation with High Performance Computing", 2018, Frontiers in physiology, 9.
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A. Ebert, R. Dutta, P. Wu, K. Mengersen and A. Mira, "Likelihood-Free Parameter Estimation for Dynamic Queueing Networks", 2018, arXiv:1804.02526
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R. Dutta, M. Schoengens, A. Ummadisingu, N. Widerman, J. P. Onnela, A. Mira, "ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation", 2017, arXiv:1711.04694
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
- know our branching strategy
- use GitHub pull requests to merge your contribution
- consider documenting your code according to the NumPy documentation style guide
- consider writing reasonable unit tests