Phylogenetic Inference
Overview
Phylogenetic inference uses observed traits to estimate evolutionary trees, including their branching patterns and divergence times, helping explain how species, genes, languages, and other evolving systems are related. Classical approaches include likelihood-based and Bayesian methods, parsimony, and distance-based algorithms; species-tree methods must also account for discordance among gene histories. Newer work uses neural networks and generative models to propose trees, estimate distances, or sample posterior distributions, sometimes incorporating tree-space geometry or multimodal data. A central challenge is scaling to large datasets while retaining accurate, calibrated uncertainty under complex evolutionary processes and avoiding poor generalization beyond training data.
Latest papers
12 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.