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Approximate Bayesian computational methods

Abstract

Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach to intractable 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 brought on the original ABC algorithm in recent years.

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Author information

Correspondence to Jean-Michel Marin.

Additional information

This research was financially supported by the French Agence Nationale de la Recherche grant ‘EMILE’ ANR-09-BLAN-0145-01, as well as by the Fondation des Sciences Mathématiques de Paris and a GIS scholarship for the fourth author.

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Marin, J., Pudlo, P., Robert, C.P. et al. Approximate Bayesian computational methods. Stat Comput 22, 1167–1180 (2012). https://doi.org/10.1007/s11222-011-9288-2

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Keywords

  • Likelihood-free methods
  • Bayesian statistics
  • ABC methodology
  • DIYABC
  • Bayesian model choice