Distributed, likelihood-free ABC-SMC inference
Project description
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.
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
Related Projects
- 🧠 Neural Posterior Estimation: BayesFlow
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