@Article{IshidaVPCSTCB:2015:LiInVi,
author = "Ishida, E. E. O. and Vitenti, S. D. P. and Penna-Lima, Mariana and
Cisewski, J. and Souza, R. S. and Trindade, A. M. M. and Cameron,
E. and Busti, V. C.",
affiliation = "{Max-Planck-Institut f{\"u}r Astrophysik} and {Institut
d'Astrophysique de Paris} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Yale University} and {EIRSA Lendulet
Astrophysics Research Group} and {Universidade do Porto} and
{University of Oxford} and Astronomy, Cosmology and Gravity Centre
(ACGC)",
title = "Cosmoabc: likelihood-free inference via population Monte Carlo
approximate bayesian computation",
journal = "Astronomy and Computing",
year = "2015",
volume = "13",
pages = "1--11",
month = "Nov.",
keywords = "(cosmology:) large-scale structure of universe, Galaxies:
statistics.",
abstract = "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 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.",
doi = "10.1016/j.ascom.2015.09.00",
url = "http://dx.doi.org/10.1016/j.ascom.2015.09.00",
issn = "2213-1337",
language = "en",
targetfile = "2015_ishida.pdf",
urlaccessdate = "15 jun. 2024"
}