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Adaptive approximate Bayesian computation for complex models

Listed author(s):
  • Maxime Lenormand


  • Franck Jabot


  • Guillaume Deffuant


Registered author(s):

    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

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    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 28 (2013)
    Issue (Month): 6 (December)
    Pages: 2777-2796

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    Handle: RePEc:spr:compst:v:28:y:2013:i:6:p:2777-2796
    DOI: 10.1007/s00180-013-0428-3
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    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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