pyABC

pyABC is a massively parallel, distributed, and scalable ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) framework for parameter estimation of complex stochastic models. It 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 integration with R and Julia.

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Resources

• 📖 Documentation: https://pyabc.rtfd.io
• 💡 Examples: https://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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