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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.
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FAQs
Distributed, likelihood-free ABC-SMC inference
We found that pyabc demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 5 open source maintainers collaborating on the project.
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