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Approximate Bayesian Computational methods

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Author Info

  • Marin, Jean-Michel
  • Pudlo, Pierre
  • Robert, Christian P.
  • Ryder, Robin
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    Abstract

    Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.

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    Bibliographic Info

    Paper provided by Paris Dauphine University in its series Economics Papers from University Paris Dauphine with number 123456789/5724.

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    Date of creation: 2012
    Date of revision:
    Publication status: Published in Statistics and Computing, 2012, Vol. 22, no. 6. pp. 1167-1180.Length: 13 pages
    Handle: RePEc:dau:papers:123456789/5724

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    Related research

    Keywords: likelihood-free methods; Bayesian statistics; ABC Methodology; DIYABC; Bayesian model chance;

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    Cited by:
    1. Michael Creel & Dennis Kristensen, 2013. "Indirect Likelihood Inference (revised)," UFAE and IAE Working Papers 931.13, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).

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