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Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry

Phylogenetic models admit polynomial parametrization maps in terms of the root distribution and transition probabilities along the edges of the phylogenetic tree. For symmetric continuous-time group-based models, Matsen studied the polynomial inequalities that characterize the joint probabilities in...

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Autores principales: Kosta, Dimitra, Kubjas, Kaie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342846/
https://www.ncbi.nlm.nih.gov/pubmed/30357599
http://dx.doi.org/10.1007/s11538-018-0523-2
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author Kosta, Dimitra
Kubjas, Kaie
author_facet Kosta, Dimitra
Kubjas, Kaie
author_sort Kosta, Dimitra
collection PubMed
description Phylogenetic models admit polynomial parametrization maps in terms of the root distribution and transition probabilities along the edges of the phylogenetic tree. For symmetric continuous-time group-based models, Matsen studied the polynomial inequalities that characterize the joint probabilities in the image of these parametrizations (Matsen in IEEE/ACM Trans Comput Biol Bioinform 6:89–95, 2009). We employ this description for maximum likelihood estimation via numerical algebraic geometry. In particular, we explore an example where the maximum likelihood estimate does not exist, which would be difficult to discover without using algebraic methods.
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spelling pubmed-63428462019-02-06 Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry Kosta, Dimitra Kubjas, Kaie Bull Math Biol Special Issue: Algebraic Methods in Phylogenetics Phylogenetic models admit polynomial parametrization maps in terms of the root distribution and transition probabilities along the edges of the phylogenetic tree. For symmetric continuous-time group-based models, Matsen studied the polynomial inequalities that characterize the joint probabilities in the image of these parametrizations (Matsen in IEEE/ACM Trans Comput Biol Bioinform 6:89–95, 2009). We employ this description for maximum likelihood estimation via numerical algebraic geometry. In particular, we explore an example where the maximum likelihood estimate does not exist, which would be difficult to discover without using algebraic methods. Springer US 2018-10-24 2019 /pmc/articles/PMC6342846/ /pubmed/30357599 http://dx.doi.org/10.1007/s11538-018-0523-2 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Special Issue: Algebraic Methods in Phylogenetics
Kosta, Dimitra
Kubjas, Kaie
Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry
title Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry
title_full Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry
title_fullStr Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry
title_full_unstemmed Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry
title_short Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry
title_sort maximum likelihood estimation of symmetric group-based models via numerical algebraic geometry
topic Special Issue: Algebraic Methods in Phylogenetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342846/
https://www.ncbi.nlm.nih.gov/pubmed/30357599
http://dx.doi.org/10.1007/s11538-018-0523-2
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