Bayesian Computation and Model Selection Without Likelihoods

Leuenberger, Christoph; Wegmann, Daniel

doi:10.1534/genetics.109.109058

Cited by 175Markov Chains and Monte Carlo MethodsGenetic diversity and population structureStatistical Methods and Bayesian Inference

Bayesian Computation and Model Selection Without Likelihoods

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Leuenberger, Christoph, Wegmann, Daniel·2010·Genetics

DOI 10.1534/genetics.109.109058PMC PMC2815920

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Abstract

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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When model selection is based on summary statistics of the data instead of the data themselves, model selection is notoriously dependent on arbitrary choices made in the set-up of the ABC inference, and can swing in favor of any plausible model, irrespective of which is the correct one.
“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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Authors: Leuenberger, C., & Wegmann, D.
Affiliations: University of Fribourg (CH)
University of Bern (CH)
Corresponding author: Leuenberger, Christoph
ORCID: Leuenberger, Christoph
Wegmann, Daniel
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
DOI: 10.1534/genetics.109.109058
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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Bayesian Computation and Model Selection Without Likelihoods

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