ReviewSystematic biology2019
Marginal Likelihoods in Phylogenetics: A Review of Methods and Applications.
Review in Systematic biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- The Phylodynamic Threshold of Measurably Evolving Populations.Molecular biology and evolution · 2026Article
- Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom.Cell systems · 2025Article
- Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.PLoS computational biology · 2024Article
- Stochastic Character Mapping, Bayesian Model Selection, and Biosynthetic Pathways Shed New Light on the Evolution of Habitat Preference in Cyanobacteria.Systematic biology · 2024Article
- Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock.Genome biology and evolution · 2024Review
- Optimizing representations for integrative structural modeling using Bayesian model selection.Bioinformatics (Oxford, England) · 2024Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.bioRxiv : the preprint server for biology · 2023Article
- An evolution-based framework for describing human gut bacteria.bioRxiv : the preprint server for biology · 2023Article
- Detecting Episodic Evolution through Bayesian Inference of Molecular Clock Models.Molecular biology and evolution · 2023Article
- Likelihood-Based Tests of Species Tree Hypotheses.Molecular biology and evolution · 2023Article
- Identifying the Best Approximating Model in Bayesian Phylogenetics: Bayes Factors, Cross-Validation or wAIC?Systematic biology · 2023Article
- A New Assessment of Robust Capuchin Monkey (Genes · 2023Article
- Evolutionary rate of SARS-CoV-2 increases during zoonotic infection of farmed mink.Virus evolution · 2023Article
- The Emergence of SARS-CoV-2 Variants of Concern Is Driven by Acceleration of the Substitution Rate.Molecular biology and evolution · 2022Article
- Discovery of Novel Herpes Simplexviruses in Wild Gorillas, Bonobos, and Chimpanzees Supports Zoonotic Origin of HSV-2.Molecular biology and evolution · 2021Article
- Under pressure: phenotypic divergence and convergence associated with microhabitat adaptations in Triatominae.Parasites & vectors · 2021Article
- Morphological Characters Can Strongly Influence Early Animal Relationships Inferred from Phylogenomic Data Sets.Systematic biology · 2021Article
- Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics.PeerJ · 2021Article
- Estimating effective population size changes from preferentially sampled genetic sequences.PLoS computational biology · 2020Article
- The comparative biogeography of Philippine geckos challenges predictions from a paradigm of climate-driven vicariant diversification across an island archipelago.Evolution; international journal of organic evolution · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics is becoming the statistical foundation of biology. The importance of model choice continues to grow as phylogenetic models continue to increase in complexity to better capture micro- and macroevolutionary processes. In a Bayesian framework, the marginal likelihood is how data update our prior beliefs about models, which gives us an intuitive measure of comparing model fit that is grounded in probability theory. Given the rapid increase in the number and complexity of phylogenetic models, methods for approximating marginal likelihoods are increasingly important. Here, we try to provide an intuitive description of marginal likelihoods and why they are important in Bayesian model testing. We also categorize and review methods for estimating marginal likelihoods of phylogenetic models, highlighting several recent methods that provide well-behaved estimates. Furthermore, we review some empirical studies that demonstrate how marginal likelihoods can be used to learn about models of evolution from biological data. We discuss promising alternatives that can complement marginal likelihoods for Bayesian model choice, including posterior-predictive methods. Using simulations, we find one alternative method based on approximate-Bayesian computation to be biased. We conclude by discussing the challenges of Bayesian model choice and future directions that promise to improve the approximation of marginal likelihoods and Bayesian phylogenetics as a whole.
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