cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation. Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://cosmoabc.readthedocs.io/en/latest/
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
- Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
- E. E. O. Ishida, S. D. P. Vitenti, M. Penna-Lima, J. Cisewski, R. S. de Souza, A. M. M. Trindade, E. Cameron, V. C. Busti, for the COIN collaboration: cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation (2015) arXiv