The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Bayesian Computation and Model Selection Without Likelihoods
Open accessAbstract
Until recently, the use of Bayesian inference was limited to a few cases because for many realistic probability models the likelihood function cannot be calculated analytically. The situation changed with the advent of likelihood-free inference algorithms, often subsumed under the term approximate Bayesian computation (ABC). A key innovation was the use of a postsampling regression adjustment, allowing larger tolerance values and as such shifting computation time to realistic orders of magnitude. Here we propose a reformulation of the regression adjustment in terms of a general linear model (GLM). This allows the integration into the sound theoretical framework of Bayesian statistics and the use of its methods, including model selection via Bayes factors. We then apply the proposed methodology to the question of population subdivision among western chimpanzees, Pan troglodytes verus.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The limitations that have been highlighted in the literature are pertinent for cases where inferences are based on summary statistics of the data instead of the data themselves, an approach conventionally adopted in population genetics applications. In these cases model selection is notoriously dependent on arbitrary choices made in the set-up of the ABC inference, and can swing in favor of any Pl”
From A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation (2014) · cites this paper as [61]
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
Arlequin (version 3.0): An integrated software package for population genetics data analysis
Excoffier, L., Laval, G., & Schneider, S. (2005) · Evolutionary Bioinformatics · doi:10.1177/117693430500100003 · Cited by 14,265
On the number of segregating sites in genetical models without recombination
Watterson, G. A. (1975) · Theoretical Population Biology · doi:10.1016/0040-5809(75)90020-9 · Cited by 3,933
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
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
An Introduction to Bayesian Inference in Econometrics.
Sargan, J. D., & Zellner, A. (1974) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344559 · Cited by 1,938
Markov 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
Inferring Coalescence Times From DNA Sequence Data
Tavaré, S., Balding, D. J., Griffiths, R., & Donnelly, P. (1997) · Genetics · doi:10.1093/genetics/145.2.505 · Cited by 941
Sequential Monte Carlo without likelihoods
Sisson, S. A., Fan, Y., & Tanaka, M. M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0607208104 · Cited by 859
Adaptive approximate Bayesian computation
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., & Robert, C. P. (2009) · Biometrika · doi:10.1093/biomet/asp052 · Cited by 614
An Introduction to Bayesian Inference in Econometrics
Good, I. J. (1975) · Technometrics · doi:10.1080/00401706.1975.10489285 · Cited by 452
Research works printed but not linked: 5
- 2007, Genome Res., 17, 1505, 10.1101/gr.6409707 · DOI
- 2005, Evol. Bioinform. Online, 1, 47
- 2006, Proc. Natl. Acad. Sci. USA, 102, 7476
- 2006, Nat. Rev. Genet., 10, 759
- 2005, Mol. Biol. Evol., 22, 297
Cited by
Sorted by citations
Items 101–175 of 175
Why to account for finite sites in population genetic studies and how to do this with Jaatha 2.0
Mathew, L. A., Staab, P. R., Rose, L. E., & Metzler, D. (2013) · Ecology and Evolution · doi:10.1002/ece3.722 · Cited by 21
Grazian, C., & Fan, Y. (2019) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1486 · Cited by 21
Chu, J., Wegmann, D., Yeh, C., Lin, R.-C., Yang, X., Lei, F.-M., Yao, C.-T., Zou, F.-S., & Li, S. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst140 · Cited by 21
Self-citationBedada, G., Westerbergh, A., Nevo, E., Korol, A., & Schmid, K. J. (2014) · Heredity · doi:10.1038/hdy.2014.2 · Cited by 20
No borders during the post‐glacial assembly of European bryophytes
Ledent, A., Désamoré, A., Laenen, B., Mardulyn, P., McDaniel, S. F., Zanatta, F., Patiño, J., & Vanderpoorten, A. (2019) · Ecology Letters · doi:10.1111/ele.13254 · Cited by 20
Finding the best resolution for the Kingman–Tajima coalescent: theory and applications
Sainudiin, R., Stadler, T., & Véber, A. (2014) · Journal of Mathematical Biology · doi:10.1007/s00285-014-0796-5 · Cited by 19
Divergence history of the Carpathian and smooth newts modelled in space and time
Zieliński, P., Nadachowska‐Brzyska, K., Dudek, K., & Babik, W. (2016) · Molecular Ecology · doi:10.1111/mec.13724 · Cited by 18
Approximate Bayesian Computation: A Survey on Recent Results
Robert, C. P. (2016) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-33507-0_7 · Cited by 19
González‐Serna, M. J., Cordero, P. J., & Ortego, J. (2019) · Molecular Ecology · doi:10.1111/mec.15086 · Cited by 19
Wielstra, B., Zieliński, P., & Babik, W. (2017) · Biological Journal of the Linnean Society · doi:10.1093/biolinnean/blx087 · Cited by 18
The genetic structure of Nautilus pompilius populations surrounding Australia and the Philippines
Williams, R. C., Jackson, B., Duvaux, L., Dawson, D. A., Burke, T. A., & Sinclair, W. (2015) · Molecular Ecology · doi:10.1111/mec.13255 · Cited by 18
Row, J. R., Brooks, R. J., MacKinnon, C. A., LAWSON, A., Crother, B. I., White, M. E., & Lougheed, S. C. (2011) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2011.02362.x · Cited by 18
Geography and end use drive the diversification of worldwide winter rye populations
Parat, F., Schwertfirm, G., Rudolph, U., Miedaner, T., Korzun, V. N., Bauer, E., Schön, C., & Tellier, A. (2015) · Molecular Ecology · doi:10.1111/mec.13495 · Cited by 18
New Insight into the Evolution of Symbiotic Genes in Black Locust-Associated Rhizobia
Liu, Z., Chen, W., Jiao, S., Wang, X., Fan, M., Wang, E. T., & Wei, G. (2019) · Genome Biology and Evolution · doi:10.1093/gbe/evz116 · Cited by 17
Strict allopatric speciation of sky island Pyrrhula erythaca species complex
Dong, F., Li, S., Chiu, C., Dong, L., Yao, C.-T., & Yang, X.-J. (2020) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2020.106941 · Cited by 20
Martin, S. H., Eriksson, A., Kozak, K. M., Manica, A., & Jiggins, C. D. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/015800 · Cited by 17
Reverse engineering gene regulatory networks using approximate Bayesian computation
Rau, A., Jaffrézic, F., Foulley, J. L., & Doerge, R. W. (2011) · Statistics and Computing · doi:10.1007/s11222-011-9309-1 · Cited by 16
Muñoz-Ramírez, C. P., Victoriano, P. F., & Habit, E. (2015) · Limnologica · doi:10.1016/j.limno.2015.04.004 · Cited by 16
Silva, N. M., Kreutzer, S., Souleles, A., Triantaphyllou, S., Kotsakis, K., Urem-Kotsou, D., Halstead, P., Efstratiou, N., Kotsos, S., Karamitrou-Mentessidi, G., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-16745-8 · Cited by 14
Self-citationDong, F., Zou, F., Lei, F., Liang, W., Li, S., & Yang, X. (2014) · Molecular Ecology · doi:10.1111/mec.12981 · Cited by 14
Sandoval‐Castellanos, E., Palkopoulou, E., & Dalén, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098011 · Cited by 14
Approximate Bayesian inference for complex ecosystems
Stumpf, M. P. H. (2016) · Cited by 13
Kutsukake, N., & Innan, H. (2014) · Springer eBooks · doi:10.1007/978-3-662-43550-2_17 · Cited by 12
Nott, D. J., Ong, V. M.-H., Fan, Y., & Sisson, S. A. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315117195-8 · Cited by 12
Approximate Bayesian computation with functional statistics
Soubeyrand, S., Carpentier, F., Guiton, F., & Klein, E. K. (2013) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2012-0014 · Cited by 12
Aeschbacher, S., Futschik, A., & Beaumont, M. (2013) · Molecular Ecology · doi:10.1111/mec.12165 · Cited by 12
Kanitz, R., Guillot, E. G., Antoniazza, S., Neuenschwander, S., & Goudet, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192460 · Cited by 11
Ferreiro, D., Branco, C., & Arenas, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae096 · Cited by 10
Dust Transport from North Africa to the Middle East: Synoptic Patterns and Numerical Forecast
Karami, S., Kaskaoutis, D. G., Pytharoulis, I., Sotiropoulou, R. P., & Tagaris, E. (2024) · Atmosphere · doi:10.3390/atmos15050531 · Cited by 10
Substructured Population Growth in the Ashkenazi Jews Inferred with Approximate Bayesian Computation
Gladstein, A. L., & Hammer, M. F. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz047 · Cited by 11
Ometto, L., Li, M., Bresadola, L., Barbaro, E., Neteler, M., & Varotto, C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125199 · Cited by 9
Pilgrim, C., & Hills, T. T. (2021) · Scientific Reports · doi:10.1038/s41598-021-96214-w · Cited by 11
Approximate Bayesian computation techniques for optical characterization of nanoparticle clusters
Eriçok, O. B., Cemgil, A. T., & Ertürk, H. (2017) · Journal of the Optical Society of America A · doi:10.1364/josaa.35.000088 · Cited by 9
Haba, Y., & Kutsukake, N. (2019) · Evolutionary Ecology · doi:10.1007/s10682-019-10011-6 · Cited by 9
Märkle, H., & Tellier, A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007668 · Cited by 9
Likelihood-free inference by penalised logistic regressionPreprint
Dutta, R., Corander, J., Samuel Kaski, A., & Gutmann, M. U. (2016) · arXiv (Cornell University) · Cited by 9
Bayesian inference for a flexible class of bivariate beta distributions
Crackel, R. C., & Flegal, J. M. (2016) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2016.1208202 · Cited by 8
A new POD-based approximate bayesian computation method to identify parameters for formed AHSS
Zeng, Y., Yu, X., & Wang, H. (2018) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2018.10.020 · Cited by 7
Adaptive Introgression Promotes Fast Adaptation In Oaks Marginal PopulationsPreprint
Goicoechea, P. G., Guillardín-Calvo, L., Fernández‐Ibarrondo, L., Valbuena‐Carabaña, M., González‐Martínez, S. C., Alı́a, R., & Kremer, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/731919 · Cited by 5
Pratdesaba, R., Segarra, C., & Aguadé, M. (2015) · Molecular Ecology · doi:10.1111/mec.13155 · Cited by 6
Approximate Bayesian computation and Bayes linear analysis: Towards high-dimensional ABCPreprint
Nott, D. J., Fan, Y., Marshall, L. A., & Sisson, S. A. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.4755 · Cited by 6
Ancient alleles drive contemporary climate adaptation in an alpine plant
Fior, S., Luqman, H., Scharmann, M., Pålsson, A., de Jonge, J., Zoller, S., Zemp, N., Gargano, D., Wegmann, D., & Widmer, A. (2025) · Science · doi:10.1126/science.adp5717 · Cited by 5
Self-citationLegacy of supervolcanic eruptions on population genetic structure of brown kiwi
Bemmels, J. B., Haddrath, O., Colbourne, R. M., Robertson, H. A., & Weir, J. T. (2022) · Current Biology · doi:10.1016/j.cub.2022.05.064 · Cited by 5
Alijani, S., Jasouri, M., Pirany, N., & Kia, H. D. (2012) · Pakistan Veterinary Journal · Cited by 5
Tsoupas, A., Reyna-Blanco, C. S., Quilodrán, C. S., Blöcher, J., Brami, M., Wegmann, D., Bürger, J., & Currat, M. (2025) · Science Advances · doi:10.1126/sciadv.adq9976 · Cited by 4
Self-citationSummary statistics and sequential methods for approximate Bayesian computation
Prangle, D. (2011) · Lancaster EPrints (Lancaster University) · Cited by 4
Buzbas, E. O., & Rosenberg, N. A. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6282 · Cited by 3
Tsutaya, T., & Yoneda, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.2468 · Cited by 3
Approximate Bayesian Computation Algorithms for Estimating Network Model ParametersPreprint
Ye, Z., & Aris‐Brosou, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/106450 · Cited by 3
Reverse Engineering Gene Networks Using Genomic Time-Course Data
Rau, A., Tel, H. I., Doerge, R. W., Craig, B. Α., Jaffrézic, F., Ghosh, J. K., & Qi, Y. (. (2010) · HAL · Cited by 2
Approximate Bayesian computation for a flexible class of bivariate beta distributionsPreprint
Crackel, R. C., & Flegal, J. M. (2014) · arXiv (Cornell University) · Cited by 2
Row, J. R. (2011) · QSpace (Queen's University Library) · Cited by 2
Kobayashi, G. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.06.025 · Cited by 1
Demographic History Inferred From an Inversion‐Rich Spruce Bark Beetle Genome
Zieliński, P., Morales-García, J., Schebeck, M., Duduman, M., & Nadachowska‐Brzyska, K. (2026) · Molecular Ecology · doi:10.1111/mec.70250 · Cited by 1
A unified multi-resolution coalescent: Markov lumpings of the Kingman-Tajima n-coalescent
Sainudiin, R., & Stadler, T. (2009) · UC Research Repository (University of Canterbury) · Cited by 1
Frantz, L. A. F., Schraiber, J. G., Madsen, O., Megens, H.-J., Cagan, A., Bosse, M., Paudel, Y., Crooijmans, R. P., Larson, G., & Groenen, M. A. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/010959 · Cited by 1
A Guide to General-Purpose Approximate Bayesian Computation SoftwarePreprint
Kousathanas, A., Duchen, P., & Wegmann, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08320 · Cited by 1
Self-citationModélisations de la dispersion du pollen et estimation à partir de marqueurs génétiques.
Carpentier, F. (2010) · doi:10.70675/76e626ffz9f9cz4babz9169z68571a78dc30 · Cited by 1
Inference From Two Non-Equilibrium Models In Population Genetics
Peter, B. M. (2014) · eScholarship (California Digital Library)
APPROXIMATE BAYESIAN APPROACHES FOR REVERSE ENGINEERING BIOLOGICAL NETWORKS
Rau, A., Jaffrézic, F., Foulley, J.-L., & Doerge, R. W. (2010) · Conference on Applied Statistics in Agriculture · doi:10.4148/2475-7772.1067
Methods and Algorithms for Inference Problems in Population Genetics
Pei, J. (2018) · OpenCommons - UConn (University of Connecticut)
Rowsey, D. M. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)
Statistical Modeling and Inference in Genetics
Wegmann, D., & Leuenberger, C. (2019) · Wiley eBooks · doi:10.1002/9781119487845.ch1
Self-citationFixing Bias in Zipf's Law Estimators Using Approximate Bayesian ComputationPreprint
Pilgrim, C., & Hills, T. T. (2020) · arXiv (Cornell University)
The discrepancy measures in approximate Bayesian computation and applications in bioscience
(2020) · Communications in Mathematical Biology and Neuroscience · doi:10.28919/cmbn/4568
Palestro, J. J., Sederberg, P. B., Osth, A. F., Zandt, T. V., & Turner, B. M. (2018) · Computational approaches to cognition and perception · doi:10.1007/978-3-319-72425-6_5
Hassan, A. R., Rengifo–Castro, J. D., Manzur, M., & Rueda-Ramírez, E. (2025) · Journal of Productivity Analysis · doi:10.1007/s11123-025-00765-3
Anàlisi multilocus del polimorfisme nucleotídic al llarg del cromosoma J de Drosophila subobscura
Pratdesaba, R. (2014) · Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona)
Tracing Neanderthal ancestry patterns through successive population expansions in EuropePreprint
Tsoupas, A., Quilodrán, C. S., Rio, J., & Currat, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.20.706963
Genomic evidence for widespread hybridization and demographic history in invasive saltcedar
Lee, S., & Choi, T. (2026) · Heredity · doi:10.1038/s41437-026-00866-1
Butlin, R. K., Saura, M., Charrier, G., Jackson, B., André, C., Caballero, A., Coyne, J. A., Galindo, J., Grahame, J., Hollander, J., et al. (2013) · Data Archiving and Networked Services (DANS) · doi:10.5061/dryad.m186r.1
Likelihood Free Inference for a Flexible Class of Bivariate Beta Distributions
Crackel, R. C. (2015) · eScholarship (California Digital Library)
Metadata
- Authors
- Leuenberger, C., & Wegmann, D.
- Affiliations
- University of Fribourg (CH)
- University of Bern (CH)
- Corresponding author
- Leuenberger, Christoph
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-01-01
- Volume
- 184
- Issue
- 1
- Pages
- 243-252
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC2815920
- OpenAlex
- W2118659960
- Cited by
- 175
- References
- 23
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 3 · 2022: 2 · 2021: 3 · 2020: 7 · 2019: 15 · 2018: 13 · 2017: 6 · 2016: 17 · 2015: 14 · 2014: 22 · 2013: 19 · 2012: 13
- FWCI
- 13.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- reroDoc Digital Library (submittedVersion)
- BORIS (University Library Bern) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- approximate Bayesian computation, simulation-based inference, regression adjustment, general linear model, model selection, Bayes factor, population subdivision, western chimpanzee, Pan troglodytes verus, population genetics
- MeSH terms
- Algorithms, Animals, Bayes Theorem, Pan troglodytes, Genetics, Population, Humans, Models, Genetic, Likelihood Functions, Linear Models
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
