This article is available for purchase or by subscription. See below.
Abstract
|
Phylogenetic varieties are algebraic varieties specified by a statistical model
describing the evolution of biological sequences along a tree. Its understanding is an
important problem in algebraic statistics, particularly in the context of phylogeny
reconstruction. In the broader area of algebra statistics, there have been important
theoretical advances in computing certain invariants associated with algebraic
varieties arising in applications. Beyond the dimension and degree of a variety, one is
interested in computing other algebraic degrees, such as the maximum likelihood
degree and the Euclidean distance degree. Despite these efforts, the current literature
lacks explicit computations of these invariants for the particular case of phylogenetic
varieties. In our work, we fill this gap by computing these invariants for phylogenetic
varieties arising from the simplest group-based models of nucleotide substitution
Cavender–Farris–Neyman model, Jukes–Cantor model, Kimura 2-parameter model
and the Kimura 3-parameter model on small phylogenetic trees with at most 5 leaves.
|
PDF Access Denied
We have not been able to recognize your IP address
3.14.249.104
as that of a subscriber to this journal.
Online access to the content of recent issues is by
subscription, or purchase of single articles.
Please contact your institution's librarian suggesting a subscription, for example by using our
journal-recommendation form.
Or, visit our
subscription page
for instructions on purchasing a subscription.
You may also contact us at
contact@msp.org
or by using our
contact form.
Or, you may purchase this single article for
USD 40.00:
Keywords
phylogenetics, euclidean distance degree, maximum
likelihood degree, numerical algebraic geometry, symbolic
computations
|
Mathematical Subject Classification
Primary: 62R01, 65H10, 92D15
Secondary: 14Q30
|
Milestones
Received: 30 September 2022
Revised: 18 January 2024
Accepted: 6 February 2024
Published: 16 May 2024
|
© 2023 MSP (Mathematical Sciences
Publishers). |
|