Distributed, likelihood-free ABC-SMC inference
Project description
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
Source: https://github.com/icb-dcm/pyabc
Bug reports: https://github.com/icb-dcm/pyabc/issues
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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