The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Approximate Bayesian Computation
Open accessAbstract
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).
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Cites
A survey of cross-validation procedures for model selectionPreprint
Arlot, S., & Célisse, A. (2010) · Cited by 3,625
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
Approximate Bayesian computation in population genetics
Beaumont, M. A., Zhang, W., & Balding, D. J. (2002) · Genetics · doi:10.1093/genetics/162.4.2025 · Cited by 2,932
Adaptive Control Processes: A Guided Tour
Bellman, R. E. (1961) · doi:10.1515/9781400874668 · Cited by 2,802
Adaptive Control Processes: A Guided Tour.
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Jaynes, E. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300117 · Cited by 1,750
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P. H. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0172 · Cited by 1,498
Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
Rubin, D. B. (1984) · The Annals of Statistics · doi:10.1214/aos/1176346785 · Cited by 1,343
Approximate Bayesian Computation (ABC) in practice
Csilléry, K., Blum, M. G. B., Gaggiotti, O. E., & François, O. (2010) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2010.04.001 · Cited by 1,302 · cited 8×
“Low-dimensional sufficient statistics are optimal for this purpose, as they capture all relevant information present in the data in the simplest possible form [11].”
BackgroundMethodResultInfluentialMarkov chain Monte Carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0306899100 · Cited by 1,199
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PyMC: a modern, and comprehensive probabilistic programming framework in Python
Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., et al. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1516 · Cited by 870
Ten simple rules for the computational modeling of behavioral data
Wilson, R. C., & Collins, A. G. (2019) · eLife · doi:10.7554/elife.49547 · Cited by 710
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
Blum, M. G. B., Nunes, M. A., Prangle, D., & Sisson, S. A. (2013) · Statistical Science · doi:10.1214/12-sts406 · Cited by 353
Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
Wilkinson, R. D. (2013) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2013-0010 · Cited by 247
Metadata
- Authors
- Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-01-10
- Volume
- 9
- Issue
- 1
- Pages
- e1002803
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2032616735
- Cited by
- 623
- References
- 62
- FWCI
- 31.54
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013). Approximate Bayesian Computation. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1002803
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