Distributed, likelihood-free ABC-SMC inference
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.
Bug reports: https://github.com/icb-dcm/pyabc/issues
Source code: https://github.com/icb-dcm/pyabc
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
pyabc-0.12.10.tar.gz (245.6 kB view hashes)
pyabc-0.12.10-py3-none-any.whl (320.5 kB view hashes)