ArticlePeerJ2019
Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 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
- Efficient Bayesian Phylogenetics under the Infinite Sites Model.bioRxiv : the preprint server for biology · 2025Article
- Phylo2Vec: A Vector Representation for Binary Trees.Systematic biology · 2025Article
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Article
- Scalable Bayesian Divergence Time Estimation With Ratio Transformations.Systematic biology · 2023Article
- 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
- Variational Phylodynamic Inference Using Pandemic-scale Data.Molecular biology and evolution · 2022Article
- Real-Time and Remote MCMC Trace Inspection with Beastiary.Molecular biology and evolution · 2022Article
- The overview of the deep learning integrated into the medical imaging of liver: a review.Hepatology international · 2021Review
- Felsenstein Phylogenetic Likelihood.Journal of molecular evolution · 2021Article
- Universal probabilistic programming offers a powerful approach to statistical phylogenetics.Communications biology · 2021Article
- Practical Speedup of Bayesian Inference of Species Phylogenies by Restricting the Space of Gene Trees.Molecular biology and evolution · 2020Article
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2 authors.
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Abstract
Recent advances in statistical machine learning techniques have led to the creation of probabilistic programming frameworks. These frameworks enable probabilistic models to be rapidly prototyped and fit to data using scalable approximation methods such as variational inference. In this work, we explore the use of the Stan language for probabilistic programming in application to phylogenetic models. We show that many commonly used phylogenetic models including the general time reversible substitution model, rate heterogeneity among sites, and a range of coalescent models can be implemented using a probabilistic programming language. The posterior probability distributions obtained via the black box variational inference engine in Stan were compared to those obtained with reference implementations of Markov chain Monte Carlo (MCMC) for phylogenetic inference. We find that black box variational inference in Stan is less accurate than MCMC methods for phylogenetic models, but requires far less compute time. Finally, we evaluate a custom implementation of mean-field variational inference on the Jukes-Cantor substitution model and show that a specialized implementation of variational inference can be two orders of magnitude faster and more accurate than a general purpose probabilistic implementation.
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