pyABC

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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

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