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 >=3.6
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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.

Examples

Many examples are available as Jupyter Notebooks in the examples directory and also for download and for online inspection in the example section of the documentation.

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Avatar for arrjon from gravatar.com arrjon Avatar for elbaraim from gravatar.com elbaraim Avatar for neuralyzer from gravatar.com neuralyzer Avatar for stephanmg from gravatar.com stephanmg Avatar for yannik.schaelte from gravatar.com yannik.schaelte

Unverified details

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Project links
Meta
  • 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 >=3.6
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0.12.16

0.12.15

0.12.13

0.12.12

0.12.11

0.12.10

0.12.9

0.12.8

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0.12.6

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0.12.4

0.12.3

0.12.2

0.12.1

0.12.0

0.11.12

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0.11.9

This version

0.11.8

0.11.7

0.11.6

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0.11.3

0.11.2

0.11.1

0.11.0

0.10.16

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0.9.26

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0.4.0

0.3.3

0.3.2

0.3.1

0.3.0

0.2.1

0.2.0

0.1.3

0.1.2

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