https://github.com/ICB-DCM/pyABC

distributed, likelihood-free inference
https://github.com/ICB-DCM/pyABC

abc approximate-bayesian-inference likelihood-free-inference parameter-inference

Last synced: 8 days ago
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distributed, likelihood-free inference

• Host: GitHub
• URL: https://github.com/ICB-DCM/pyABC
• Owner: ICB-DCM
• License: bsd-3-clause
• Created: 2017-07-12T10:30:10.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2024-10-24T14:52:47.000Z (15 days ago)
• Last Synced: 2024-10-25T16:26:27.419Z (14 days ago)
• Topics: abc, approximate-bayesian-inference, likelihood-free-inference, parameter-inference
• Language: Python
• Homepage: https://pyabc.rtfd.io
• Size: 46.4 MB
• Stars: 204
• Watchers: 6
• Forks: 44
• Open Issues: 40
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE.txt

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• awesome-sciml - ICB-DCM/pyABC: distributed, likelihood-free inference

README

        
pyABC

=====

.. figure:: https://raw.githubusercontent.com/ICB-DCM/pyABC/main/doc/logo/logo.svg

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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.

- **Documentation:** https://pyabc.rtfd.io

- **Examples:** http://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

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   :alt: PyPI

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