pyABC

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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

Related Projects

  • Parameter Estimation with Likelihoods: pyPESTO
  • Neural Posterior Estimation: BayesFlow

Project links

Key dates

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Latest release

5 maintainers

PyPI data
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Credits

Author: The pyABC developers

Maintainer: Jonas Arruda

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.11

Provides Extra

webserver-flask webserver-dash pyarrow r julia copasi ot petab amici yaml2sbml migrate plotly autograd examples doc test

Tags

likelihood-free inference abc approximate Bayesian computation sge distributed

Classifiers

Report project as malware

Release files for pyabc 0.13.0

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Source distribution for pyabc 0.13.0
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Table of built distributions (wheels) for pyabc 0.13.0
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Total release size: 671.3 kB

Release files / pyabc-0.13.0.tar.gz

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0.13.0 This release

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