ArticleSystematic biology2014
Posterior predictive Bayesian phylogenetic model selection.
Article in Systematic biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 66 citations in OpenAlex.
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- Bayesian spatial-temporal analysis and determinants of cardiovascular diseases in Tanzania mainland.BMC medical research methodology · 2024Article
- Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock.Genome biology and evolution · 2024Review
- Identifying the Best Approximating Model in Bayesian Phylogenetics: Bayes Factors, Cross-Validation or wAIC?Systematic biology · 2023Article
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- Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation.PloS one · 2022Article
- Relative model selection of evolutionary substitution models can be sensitive to multiple sequence alignment uncertainty.BMC ecology and evolution · 2021Article
- Harnessing machine learning to guide phylogenetic-tree search algorithms.Nature communications · 2021Article
- The Multispecies Coalescent Model Outperforms Concatenation Across Diverse Phylogenomic Data Sets.Systematic biology · 2020Article
- 19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology.Systematic biology · 2020Article
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- Marginal Likelihoods in Phylogenetics: A Review of Methods and Applications.Systematic biology · 2019Review
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- Phylodynamic Model Adequacy Using Posterior Predictive Simulations.Systematic biology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand-Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data.
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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.