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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.
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“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]
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Robust Demographic Inference from Genomic and SNP Data
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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
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