ArticleSystematic biology2020
19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology.
Article in Systematic biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Trial
- The Phylodynamic Threshold of Measurably Evolving Populations.Molecular biology and evolution · 2026Article
- torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.Systematic biology · 2026Article
- Phylogeographic and Host Interface Analyses Reveal the Evolutionary Dynamics of SAT3 Foot-And-Mouth Disease Virus.Viruses · 2025Article
- Phylogenetic networks empower biodiversity research.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Infinite Mixture Models for Improved Modeling of Across-Site Evolutionary Variation.Molecular biology and evolution · 2025Article
- An introduction to Sequential Monte Carlo for Bayesian inference and model comparison-with examples for psychology and behavioral science.Behavior research methods · 2025Article
- Finding high posterior density phylogenies by systematically extending a directed acyclic graph.Algorithms for molecular biology : AMB · 2025Article
- Molecular epidemiology, evolution, and transmission dynamics of raccoon rabies virus in Connecticut.Virus evolution · 2025Article
- Finding high posterior density phylogenies by systematically extending a directed acyclic graph.ArXiv · 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
- Differentiable phylogeneticsBioinformatics advances · 2024Article
- Detecting Episodic Evolution through Bayesian Inference of Molecular Clock Models.Molecular biology and evolution · 2023Article
- Bayesian phylodynamic analysis reveals the evolutionary history and the dispersal patterns of citrus tristeza virus in China based on the p25 gene.Virology journal · 2023Article
- A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm.Algorithms for molecular biology : AMB · 2023Article
- Likelihood-Based Tests of Species Tree Hypotheses.Molecular biology and evolution · 2023Article
- Statistical evaluation of character support reveals the instability of higher-level dinosaur phylogeny.Scientific reports · 2023Article
- Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.Genome biology and evolution · 2023Article
- Fidelity of hyperbolic space for Bayesian phylogenetic inference.PLoS computational biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here, we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real data sets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.