@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 = "27 abr. 2024"
}