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Approximate Bayesian computational methods

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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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Authors and Affiliations

  1. I3M, UMR CNRS 5149, Université Montpellier 2, Montpellier, France

    Jean-Michel Marin & Pierre Pudlo

  2. CEREMADE, Université Paris Dauphine and Crest INSEE, Paris, France

    Christian P. Robert & Robin J. Ryder

Authors
  1. Jean-Michel Marin
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  2. Pierre Pudlo
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  3. Christian P. Robert
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  4. Robin J. Ryder
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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, JM., 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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