Date of Award
5-17-2020
Document Type
Dissertation
Abstract
This thesis presents two projects in mathematical phylogenetics. The first presents a new, statistically consistent, fast method for inferring species trees from topological gene trees under the multispecies coalescent model. The algorithm of this method takes a collection of unrooted topological gene trees, computes a novel intertaxon distance from them, and outputs a metric species tree. The second establishes that numerical and non-numerical parameters of a specic Prole Mixture Model of protein sequence evolution are generically identifiable. Algebraic techniques are used, especially a theorem of Kruskal on tensor decomposition.
Recommended Citation
Yourdkhani, Samaneh, "Investigations in phylogenetics: tree inference and model identifiability" (2020). Mathematics and Statistics . 43.
https://ualaska.researchcommons.org/uaf_grad_math_stats/43
Handle
http://hdl.handle.net/11122/11303