Massively parallel, distributed and scalable ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) for parameter estimation of complex stochastic models. Provides numerous state-of-the-art algorithms for efficient, accurate, robust likelihood-free inference, described in the documentation and illustrated in example notebooks. Written in Python with support for especially R and Julia.
- Documentation: https://pyabc.rtfd.io
- Examples: http://pyabc.rtfd.io/en/latest/examples.html
- Contact: https://pyabc.rtfd.io/en/latest/about.html
- Bug reports: https://github.com/icb-dcm/pyabc/issues
- Source code: https://github.com/icb-dcm/pyabc
- Cite: https://pyabc.rtfd.io/en/latest/cite.html