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Approximate Bayesian Computation

Author

Listed:
  • Mikael Sunnåker
  • Alberto Giovanni Busetto
  • Elina Numminen
  • Jukka Corander
  • Matthieu Foll
  • Christophe Dessimoz
Abstract
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

Suggested Citation

  • Mikael Sunnåker & Alberto Giovanni Busetto & Elina Numminen & Jukka Corander & Matthieu Foll & Christophe Dessimoz, 2013. "Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-10, January.
    • Handle: RePEc:plo:pcbi00:1002803
    DOI: 10.1371/journal.pcbi.1002803
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Mathias Silva, 2023. "Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach [Working Papers / Documents de travail]," Working Papers hal-04066544, HAL.
    2. Frederick Callaway & Antonio Rangel & Thomas L Griffiths, 2021. "Fixation patterns in simple choice reflect optimal information sampling," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-29, March.
    3. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    4. Sifat A Moon & Lee W Cohnstaedt & D Scott McVey & Caterina M Scoglio, 2019. "A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    5. Tracy L Stepien & Holley E Lynch & Shirley X Yancey & Laura Dempsey & Lance A Davidson, 2019. "Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
    6. Farmer, J. Doyne & Dyer, Joel & Cannon, Patrick & Schmon, Sebastian, 2022. "Black-box Bayesian inference for economic agent-based models," INET Oxford Working Papers 2022-05, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    7. Jonathan U Harrison & Ruth E Baker, 2020. "An automatic adaptive method to combine summary statistics in approximate Bayesian computation," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    8. Clark, Matt & Andrews, Jeffrey & Kolarik, Nicholas & Omar, Mbarouk Mussa & Hillis, Vicken, 2024. "Causal attribution of agricultural expansion in a small island system using approximate Bayesian computation," Land Use Policy, Elsevier, vol. 137(C).
    9. Dyer, Joel & Cannon, Patrick & Farmer, J. Doyne & Schmon, Sebastian M., 2024. "Black-box Bayesian inference for agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 161(C).
    10. Johnston Iain G., 2014. "Efficient parametric inference for stochastic biological systems with measured variability," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 379-390, June.
    11. Ye Chen & Ilya O. Ryzhov, 2020. "Technical Note—Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference," Operations Research, INFORMS, vol. 68(1), pages 295-307, January.
    12. Chapron, Guillaume & Wikenros, Camilla & Liberg, Olof & Wabakken, Petter & Flagstad, Øystein & Milleret, Cyril & Månsson, Johan & Svensson, Linn & Zimmermann, Barbara & Åkesson, Mikael & Sand, Håkan, 2016. "Estimating wolf (Canis lupus) population size from number of packs and an individual based model," Ecological Modelling, Elsevier, vol. 339(C), pages 33-44.
    13. Warne, David J. & Baker, Ruth E. & Simpson, Matthew J., 2018. "Multilevel rejection sampling for approximate Bayesian computation," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 71-86.
    14. Aushev, Alexander & Pesonen, Henri & Heinonen, Markus & Corander, Jukka & Kaski, Samuel, 2022. "Likelihood-free inference with deep Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    15. George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.
    16. Ricardo Kanitz & Elsa G Guillot & Sylvain Antoniazza & Samuel Neuenschwander & Jérôme Goudet, 2018. "Complex genetic patterns in human arise from a simple range-expansion model over continental landmasses," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
    17. David J Price & Alexandre Breuzé & Richard Dybowski & Piero Mastroeni & Olivier Restif, 2017. "An efficient moments-based inference method for within-host bacterial infection dynamics," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-27, November.
    18. Wilkinson Richard David, 2013. "Approximate Bayesian computation (ABC) gives exact results under the assumption of model error," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(2), pages 129-141, May.
    19. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
    20. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2022. "Effective experience rating for large insurance portfolios via surrogate modeling," Papers 2211.06568, arXiv.org, revised Jun 2024.
    21. Emma Saulnier & Olivier Gascuel & Samuel Alizon, 2017. "Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-31, March.
    22. Bo H. Lindqvist & Rasmus Erlemann & Gunnar Taraldsen, 2022. "Conditional Monte Carlo revisited," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 943-968, September.
    23. Ljubisa Miskovic & Jonas Béal & Michael Moret & Vassily Hatzimanikatis, 2019. "Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-29, August.
    24. Daniel Silk & Paul D W Kirk & Chris P Barnes & Tina Toni & Michael P H Stumpf, 2014. "Model Selection in Systems Biology Depends on Experimental Design," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
    25. Hazelton, Martin L. & Cox, Murray P., 2016. "Bandwidth selection for kernel log-density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 56-67.

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