Sentence examples for maximum likelihood often from inspiring English sources

Despite this recent progress, regions of the topology are still uncertain, as different data types (e.g. nuclear genes, mitochondrial genes, morphology) and methods of analysis (e.g. maximum parsimony, maximum likelihood) often support conflicting relationships.

However, the maximum likelihood score often overfits the data because it does not reflect the model complexity.

Two main methods, one involving moments and the other involving maximum likelihood, are often used to estimate the parameters μ and θ.

For instance, simulations have shown that maximum likelihood methods often more accurately reconstructed the evolution of an alignment than distance or parsimony methods [ 3, 4], but could also fail in conditions where compositional biases (a condition here referred to as non-homogeneity) or rate heterogeneity along branches (a phenomenon named heterotachy, [ 5]) were too intense [ 6- 8].

However, methods for including ancillary data sources, such as the use of prior probabilities in maximum likelihood classification, are often problematic in practice.

Finding the maximum likelihood estimator is often a good solution to such a statistical inference problem.

Marginal maximum likelihood estimation is often used for estimating the parameters of the model.

This criteria is also called conditional maximum likelihood estimation (CMLE), often used in discriminative training methods, e.g. logistic regression.

These estimation problems may be due to the way the method was implemented in Stata where convergence to a maximum likelihood estimate was often not achieved.

Optimal techniques based on maximum likelihood [9] are often applicable but might be computationally prohibitive.

To recover transmitted SCI, a maximum likelihood (ML) criterion is often used as the standard detection approach [7 10].