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://goo.gl/SmB8EX
