Date: Tue, 30 May 2023 00:18:09 +0300 (EEST) Message-ID: <2058278628.9627.1685395089868@wiki-1.it.helsinki.fi> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_9626_1227623576.1685395089868" ------=_Part_9626_1227623576.1685395089868 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Method, maximum likelihood

# Method, maximum likelihood

The maximum likelihood method is a general principle in statisti= cs wherein the statistical hypothesis that assigns the highest probability = to the observed data is preferred. A statistical hypothesis assigns a proba= bility value (in the case of discrete data, a probability between 0 and 1, = or in the case of continuous data, a non-negative probability density) to a= ll conceivable data. The probability value assigned to the observed data is= called the likelihood of the hypothesis. The hypothesis can consist of str= uctural components such as a tree topology or parameters such as edg= e lengths, or both. Maximum likelihood is generally considered superior= to many other approaches due to its theoretically and empirically observed= favourable properties.

Phylogenetic trees that are based on a specific sequence evolution model= such as the Jukes=E2=80=93Cantor model<= /a> can be estimated using maximum likelihood (Felsenstein 1981). This may = require some approximations to make the inference computationally tractable= .

Reference

=E2=80=93 Felsenstein, Joseph. 1981. =E2=80=9CEvolutionary trees from DN= A sequences: A maximum likelihood approach.=E2=80=9D Journal of Mo= lecular Evolution 17: 368=E2=80=93376.

#### In other languages GE: maximale WahrscheinlichkeitFR: maximum de vraisemblance  IT: massima verosimiglianza TR

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