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Adaptive approximate Bayesian computation for complex models

Author

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  • Maxime Lenormand
  • Franck Jabot
  • Guillaume Deffuant

Abstract

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

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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. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    3. 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.
    4. 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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    2. David B. Stern & Nathan W. Anderson & Juanita A. Diaz & Carol Eunmi Lee, 2022. "Genome-wide signatures of synergistic epistasis during parallel adaptation in a Baltic Sea copepod," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Lorenzo Pacchiardi & Pierre Künzli & Marcel Schöngens & Bastien Chopard & Ritabrata Dutta, 2021. "Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 288-317, May.
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    5. Chen, C.C.-M. & Drovandi, C.C. & Keith, J.M. & Anthony, K. & Caley, M.J. & Mengersen, K.L., 2017. "Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef," Ecological Modelling, Elsevier, vol. 364(C), pages 113-123.
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    8. Wenlong He & Peng Xia & Xinan Zhang & Tianhai Tian, 2022. "Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data," Mathematics, MDPI, vol. 10(24), pages 1-15, December.
    9. Tatiana Dmitrieva & Kristin McCullough & Nader Ebrahimi, 2021. "Improved approximate Bayesian computation methods via empirical likelihood," Computational Statistics, Springer, vol. 36(2), pages 1533-1552, June.
    10. Brenda N Vo & Christopher C Drovandi & Anthony N Pettitt & Graeme J Pettet, 2015. "Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-22, December.
    11. Richard G. Everitt, 2018. "Efficient importance sampling in low dimensions using affine arithmetic," Computational Statistics, Springer, vol. 33(1), pages 1-29, March.

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