ArticleMolecular biology and evolution2019
Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model.
Article in Molecular biology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- VINE: Variational inference for scalable Bayesian reconstruction of species and cell-lineage phylogenies.bioRxiv : the preprint server for biology · 2026Article
- torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.Systematic biology · 2026Article
- Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.Genome biology and evolution · 2023Article
- Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023Article
- Article
- Scalable Bayesian phylogenetics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Felsenstein Phylogenetic Likelihood.Journal of molecular evolution · 2021Article
- Mirage: estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families.Bioinformatics advances · 2021Article
- Improving the performance of Bayesian phylogenetic inference under relaxed clock models.BMC evolutionary biology · 2020Article
- 19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology.Systematic biology · 2020Article
- Revisiting metazoan phylogeny with genomic sampling of all phyla.Proceedings. Biological sciences · 2019Article
- Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics.PeerJ · 2019Article
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2 authors.
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Abstract
The pattern of molecular evolution varies among gene sites and genes in a genome. By taking into account the complex heterogeneity of evolutionary processes among sites in a genome, Bayesian infinite mixture models of genomic evolution enable robust phylogenetic inference. With large modern data sets, however, the computational burden of Markov chain Monte Carlo sampling techniques becomes prohibitive. Here, we have developed a variational Bayesian procedure to speed up the widely used PhyloBayes MPI program, which deals with the heterogeneity of amino acid profiles. Rather than sampling from the posterior distribution, the procedure approximates the (unknown) posterior distribution using a manageable distribution called the variational distribution. The parameters in the variational distribution are estimated by minimizing Kullback-Leibler divergence. To examine performance, we analyzed three empirical data sets consisting of mitochondrial, plastid-encoded, and nuclear proteins. Our variational method accurately approximated the Bayesian inference of phylogenetic tree, mixture proportions, and the amino acid propensity of each component of the mixture while using orders of magnitude less computational time.
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