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Adaptive approximate Bayesian computation for complex models


  • Maxime Lenormand


  • Franck Jabot


  • Guillaume Deffuant



We propose a new approximate Bayesian computation (ABC) algorithm that aims at minimizing the number of model runs for reaching a given quality of the posterior approximation. This algorithm automatically determines its sequence of tolerance levels and makes use of an easily interpretable stopping criterion. Moreover, it avoids the problem of particle duplication found when using a MCMC kernel. When applied to a toy example and to a complex social model, our algorithm is 2–8 times faster than the three main sequential ABC algorithms currently available. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Maxime Lenormand & Franck Jabot & Guillaume Deffuant, 2013. "Adaptive approximate Bayesian computation for complex models," Computational Statistics, Springer, vol. 28(6), pages 2777-2796, December.
  • Handle: RePEc:spr:compst:v:28:y:2013:i:6:p:2777-2796
    DOI: 10.1007/s00180-013-0428-3

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    References listed on IDEAS

    1. C. C. Drovandi & A. N. Pettitt, 2011. "Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation," Biometrics, The International Biometric Society, vol. 67(1), pages 225-233, March.
    2. Joyce Paul & Marjoram Paul, 2008. "Approximately Sufficient Statistics and Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-18, August.
    3. Mark A. Beaumont & Jean-Marie Cornuet & Jean-Michel Marin & Christian P. Robert, 2009. "Adaptive approximate Bayesian computation," Biometrika, Biometrika Trust, vol. 96(4), pages 983-990.
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    1. repec:eee:ecomod:v:364:y:2017:i:c:p:113-123 is not listed on IDEAS
    2. repec:eee:ecomod:v:306:y:2015:i:c:p:278-286 is not listed on IDEAS
    3. repec:eee:ecomod:v:360:y:2017:i:c:p:425-436 is not listed on IDEAS
    4. repec:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0729-z is not listed on IDEAS


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