Statistics and Computing

, Volume 22, Issue 6, pp 1167–1180

Approximate Bayesian computational methods

  • Jean-Michel Marin
  • Pierre Pudlo
  • Christian P. Robert
  • Robin J. Ryder
Article

DOI: 10.1007/s11222-011-9288-2

Cite this article as:
Marin, JM., Pudlo, P., Robert, C.P. et al. Stat Comput (2012) 22: 1167. doi:10.1007/s11222-011-9288-2

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.

Keywords

Likelihood-free methodsBayesian statisticsABC methodologyDIYABCBayesian model choice

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jean-Michel Marin
    • 1
  • Pierre Pudlo
    • 1
  • Christian P. Robert
    • 2
  • Robin J. Ryder
    • 2
  1. 1.I3M, UMR CNRS 5149Université Montpellier 2MontpellierFrance
  2. 2.CEREMADEUniversité Paris Dauphine and Crest INSEEParisFrance