Efficient Maximum Likelihood Tree Building Methods - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Chapitre D'ouvrage Année : 2020

Efficient Maximum Likelihood Tree Building Methods

Alexandros Stamatakis
  • Fonction : Auteur
  • PersonId : 1083063
Alexey M Kozlov
  • Fonction : Auteur
  • PersonId : 1083064

Résumé

The number of possible unrooted binary trees (phylogenies) increases super-exponentially with the number of taxa. To find the Maximum Likelihood (ML) tree one has to enumerate and evaluate all these trees. As we will see, this is computationally not feasible. Therefore, one predominantly deploys ad hoc tree search methods that strive to find a "good" ML tree in the hope that it will be close, either with respect to the likelihood score or the topological structure, to the globally optimal ML tree. In this chapter we provide an overview over the most popular and efficient ML tree search techniques. How to cite: Alexandros Stamatakis and Alexey M.
Fichier principal
Vignette du fichier
chapter_1.2_stamatakis_v2.pdf (402.45 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-02535285 , version 1 (10-04-2020)
hal-02535285 , version 2 (26-11-2020)

Identifiants

  • HAL Id : hal-02535285 , version 2

Citer

Alexandros Stamatakis, Alexey M. Kozlov, Alexey M Kozlov. Efficient Maximum Likelihood Tree Building Methods. Scornavacca, Celine; Delsuc, Frédéric; Galtier, Nicolas. Phylogenetics in the Genomic Era, No commercial publisher | Authors open access book, pp.1.2:1--1.2:18, 2020. ⟨hal-02535285v2⟩

Collections

PGE
1147 Consultations
1688 Téléchargements

Partager

Gmail Facebook X LinkedIn More