Sentence examples for maximum likelihood character from inspiring English sources

The maximum parsimony and maximum likelihood character state reconstructions revealed that the characteristic maternal mouthbrooding behavior, where only the females incubate their fry in their buccal cavities, evolved in the common ancestor of the CSA lineage, the Pseudocrenilabrus-, the Astatoreochromis-lineage, and the modern haplochromines (see asterisk in Fig. 3).

The hypothesis that stylasterid corals originated in deep waters is strongly supported by maximum-likelihood character state reconstructions (Figure 2).

To better understand the evolution of these ontogenetic trajectories we used a recently published molecular phylogeny [ 28] and maximum likelihood ancestral character state reconstruction [ 42].

Information on sexual system was compiled and Maximum Parsimony (MP) and model-based Maximum Likelihood (ML) character mapping approaches were used on the phylogeny to investigate sexual system evolution across the order.

Mapping the phylogenetic tree (with maximum likelihood ancestral character estimation) onto the space of the first three shape PCs, each dietary group represents an independent direction of shape variation, with few convergences in the separate lineages.

It is usually performed by maximum likelihood (ML) on characters aligned either pairwise or jointly in a multiple sequence alignment (MSA).

In contrast, the composition of the reconstructed states of the hypothetical ancestor of Polyzoa and Brachiozoa, the sister group of Polyzoa in the constrained maximum likelihood tree, for this character subset is not significantly deviating (Table  1).

The program contml (continuous character maximum likelihood) in phylip 3.65 [ 25] used the 149 canonical two-base through five-base motif frequencies to construct three maximum-likelihood trees (Figs. 3, 4, and 5), one per level of allowed mismatches, that provide a convenient basis to consider intergeneric similarities.

One way around these difficulties is the maximum likelihood approach, which assigns characters to terminal nodes and probability distributions for those characters to internal nodes (Felsenstein, 1981).

This model implements a maximum likelihood approach to variable characters (i.e. morphology).

We note that MP gives superior performance when evolutionary rate is variable for the set of characters and approximates maximum likelihood (ML) when using large numbers of multistate characters (convergence is less likely with large number of character states) [ 54].