The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Approximate Bayesian computational methods
Open accessAbstract
Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach to intractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions brought on the original ABC algorithm in recent years.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Monte Carlo Statistical Methods
Kim, H. (2000) · Technometrics · doi:10.1080/00401706.2000.10485722 · Cited by 5,508
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Monte Carlo Statistical Methods
Robert, C. P., & Casella, G. (2004) · Springer Texts in Statistics · doi:10.1007/978-1-4757-4145-2 · Cited by 4,271
Self-citationApproximate 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
Accurate Approximations for Posterior Moments and Marginal Densities
Tierney, L., & Kadane, J. B. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478240 · Cited by 2,129
Rue, H., & Held, L. (2005) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203492024 · Cited by 1,823
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
Cited by
Approximate Bayesian Computation
Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002803 · Cited by 623
Guillot, G., & Rousset, F. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12018 · Cited by 537
Lack of confidence in approximate Bayesian computation model choice
Robert, C. P., Cornuet, J.-M., Marin, J.-M., & Pillai, N. S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102900108 · Cited by 317
Self-citationA General Framework for Updating Belief Distributions
Bissiri, P. G., Holmes, C. C., & Walker, S. G. (2016) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12158 · Cited by 285
Gilman, D., Birrer, S., Nierenberg, A., Treu, T., Du, X., & Benson, A. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3480 · Cited by 268
Metadata
- Authors
- Marin, J.-M., Pudlo, P., Robert, C. P., & Ryder, R. J.
- Venue
- Statistics and Computing
- Published
- 2011-10-21
- Volume
- 22
- Issue
- 6
- Pages
- 1167-1180
- ISSN
- 0960-3174 (ISSN-L)
- 1573-1375
- OpenAlex
- W2139812092
- Cited by
- 818
- References
- 56
- FWCI
- 31.44
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Approximate Bayesian computational methods. https://app.cochat.ai/papers/10.1007/s11222-011-9288-2
- The paper
- Marin, J.-M., Pudlo, P., Robert, C. P., & Ryder, R. J. (2012). Approximate Bayesian computational methods. Statistics and Computing. https://doi.org/10.1007/s11222-011-9288-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
