Skip to Main content Skip to Navigation
Book sections

Efficient Maximum Likelihood Tree Building Methods

Abstract : 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.
Complete list of metadatas

Cited literature [43 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02535285
Contributor : Christine Bibal <>
Submitted on : Friday, April 10, 2020 - 2:47:36 PM
Last modification on : Wednesday, April 15, 2020 - 9:37:53 AM

File

chapter_1.2_stamatakis.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02535285, version 1

Collections

PGE

Citation

Alexandros Stamatakis, 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-02535285⟩

Share

Metrics

Record views

638

Files downloads

663