Distributed, likelihood-free ABC-SMC inference

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  • License: BSD License (BSD-3-Clause)
  • Author: The pyABC developers
  • Maintainer: Yannik Schaelte
  • Tags likelihood-free, inference, abc, approximate bayesian computation, sge, distributed
  • Requires: Python >=3.10
  • Provides-Extra: r, amici, autograd, copasi, doc, examples, julia, migrate, ot, petab, plotly, pyarrow, test, test-petab, webserver-dash, webserver-flask, yaml2sbml
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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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