Sentence examples for maximum likelihood at from inspiring English sources

Transverse fracture was shown to correlate most highly with kinetic energy with a maximum likelihood at mv2=292 where m is mass (g) and v is velocity (m/s).

The duplication model achieved its maximum likelihood at 15 ancestral chromosomes for all three trees, whereas the static model achieved its maximum likelihood at 17 (table 1).

Given are the global maximum likelihood at the root node, the AIC, and the Akaike weight (w) of the respective dispersal models.

Before the ALTree localization algorithm was launched, ancestral sequences were reconstructed at each internal tree node; that is, short haplotypes were inferred with maximum likelihood at all nodes that were not leaves.

The trend model reached its maximum likelihood at chromosome counts between 16 and 20 for the three different trees and although the likelihoods decreased precipitously for lower ancestral chromosome counts, they decreased more gradually for higher counts.

In spite of losing some information when compared to ν (in the same way that distance methods are less efficient than maximum likelihood at reconstructing trees; e.g., Hasegawa et al. 1991), nucleotide diversity π was highly correlated with ν.

We compared the resulting global maximum likelihoods at the root node and the AIC and Akaike weight between models (Table 2).

3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels.

It is well known that it is impossible for complex orthogonal space-time block codes with full diversity and full rate to have more than two transmit antennas while non-orthogonal designs will lose the simplicity of maximum likelihood decoding at receivers.

This reduced dimension allows the use of optimum receivers for each group based on maximum likelihood detection at a reasonable computational cost.

The authors present an unified framework for list-based iterative multiuser detection, striking a good balance between performance and complexity as compared to the traditional linear and joint maximum likelihood detectors at the two extremes.