Distributed, likelihood-free ABC-SMC inference
Project description
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
Bug reports: https://github.com/icb-dcm/pyabc/issues
Source code: https://github.com/icb-dcm/pyabc
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