Identifiability in Phylogenetics using Algebraic Matroids

Hollering, Benjamin; Sullivant, Seth

doi:10.48550/arxiv.1909.13754

Cited by 1 publication

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“…Although computational work may suggest whether it holds or fails, parameter identifiability can only be established theoretically as it is a model property, and not dependent on an inference method. In recent years algebraic methods have been introduced and successfully applied to a number of phylogenetic mixture models, see, for example, Allman and Rhodes [2006, Allman et al [2010Allman et al [ , 2011Allman et al [ , 2019, Chifman and Kubatko [2015], Long and Sullivant [2015], Hollering and Sullivant [2019], Wascher and Kubatko [2020]. While one of these works [Rhodes and Sullivant, 2012] established a rather general result on parameter identifiability of phylogenetic mixture models with many components, it unfortunately does not apply to the profile mixture model's specific structure.…”

Section: Introductionmentioning

confidence: 99%

Parameter identifiability for a profile mixture model of protein evolution

Yourdkhani¹,

Allman²,

Rhodes³

2020

Preprint

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A Profile Mixture Model is a model of protein evolution, describing sequence data in which sites are assumed to follow many related substitution processes on a single evolutionary tree. The processes depend in part on different amino acid distributions, or profiles, varying over sites in aligned sequences. A fundamental question for any stochastic model, which must be answered positively to justify model-based inference, is whether the parameters are identifiable from the probability distribution they determine. Here we show that a Profile Mixture Model has identifiable parameters under circumstances in which it is likely to be used for empirical analyses. In particular, for a tree relating 9 or more taxa, both the tree topology and all numerical parameters are generically identifiable when the number of profiles is less than 74.

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