ArticleSystematic biology2023
Identifying the Best Approximating Model in Bayesian Phylogenetics: Bayes Factors, Cross-Validation or wAIC?
Article in Systematic biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Forecasting the future of American mass shootings with Bayesian agent-based model calibration.PNAS nexus · 2026Article
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- Unidentifiability and false-positive inference in state-dependent diversification models.Nature communications · 2026Article
- A comparative approach to analyzing and forecasting carbon dioxide emissions in ethiopia using Bayesian autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models.Carbon balance and management · 2026Article
- Toward a genomic understanding of the tree of life.Molecular biology and evolution · 2026Article
- Estimating Ancestral States of Complex Characters: A Case Study on the Evolution of Feathers.Systematic biology · 2026Article
- Hierarchical Bayesian Regression for experimental psychology: a case study of cognitive control.Frontiers in psychology · 2026Article
- Uncertainties in the phylogeny and biogeography of cave crickets.Communications biology · 2025Article
- Reply to: Uncertainties in the phylogeny and biogeography of cave crickets.Communications biology · 2025Article
- Reconstructing the evolutionary history of stoneflies: Phylogenetic insights and temporal dynamics.iScience · 2025Article
- Geostatistical analysis to guide treatment decisions for soil-transmitted helminthiasis control in Uganda.PLoS neglected tropical diseases · 2025Article
- Bayesian StairwayPlot for Inferring Single Population Demographic Histories From Site Frequency Spectra.Molecular ecology resources · 2025Article
- Stochastic Character Mapping: An Under-Exploited Approach to the Study of Molecular Evolution.Journal of molecular evolution · 2025Article
- Infinite Mixture Models for Improved Modeling of Across-Site Evolutionary Variation.Molecular biology and evolution · 2025Article
- Annotated Bioinformatic Pipelines for Phylogenomic Placement of Mitochondrial Genomes.Bio-protocol · 2025Article
- Drinking intention-behavior links vary by affect among heavy-drinking young adults: An ecological momentary assessment and transdermal sensor study.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Modeling compositional heterogeneity resolves deep phylogeny of flowering plants.Plant diversity · 2025Article
- Blouch: Bayesian Linear Ornstein-Uhlenbeck Models for Comparative Hypotheses.Systematic biology · 2024Article
- Revisiting the four Hexapoda classes: Protura as the sister group to all other hexapods.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Abstract
There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation, and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross-validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, CV represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard Markov chain Monte Carlo runs under the posterior distribution. [Bayes factor; cross-validation; marginal likelihood; model comparison; wAIC.].
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