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.

GE: maximale Wahrscheinlichkeit

FR: maximum de vraisemblance **IT: massima verosimiglianza**