cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

Understand

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.