Metadata-Version: 2.1
Name: pyabc
Version: 0.10.4
Summary: Distributed, likelihood-free ABC-SMC inference
Home-page: https://github.com/icb-dcm/pyabc
Author: Emmanuel Klinger, Yannik Schälte, Elba Raimundez
Author-email: yannik.schaelte@gmail.com
License: BSD-3-Clause
Keywords: likelihood-free inference,abc,approximate bayesian computation,sge,distributed
Platform: all
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
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# pyABC

<img src="https://raw.githubusercontent.com/ICB-DCM/pyABC/master/doc/logo/logo.png" alt="pyABC logo" width="30%"/>

[![CI](https://github.com/ICB-DCM/pyABC/workflows/CI/badge.svg)](https://github.com/ICB-DCM/pyABC/actions)
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Massively parallel, distributed and scalable ABC-SMC
(Approximate Bayesian Computation - Sequential Monte Carlo)
for parameter estimation of complex stochastic models.
Implemented in Python with support of the R language.

- **Documentation:** [https://pyabc.readthedocs.io](https://pyabc.readthedocs.io)
- **Contact:** [https://pyabc.readthedocs.io/en/latest/about.html](https://pyabc.readthedocs.io/en/latest/about.html)
- **Source:** [https://github.com/icb-dcm/pyabc](https://github.com/icb-dcm/pyabc)
- **Bug reports:** [https://github.com/icb-dcm/pyabc/issues](https://github.com/icb-dcm/pyabc/issues)

## Examples

Many examples are available as Jupyter Notebooks in the
[examples directory](https://github.com/icb-dcm/pyabc/tree/master/doc/examples)
and also for download and for online inspection in the
[example section of the documentation](http://pyabc.readthedocs.io/en/latest/examples.html).


