Sentence examples for maximum likelihood approaches from inspiring English sources

Several adaptations of maximum likelihood approaches to incremental map learning have been proposed recently.

GMMs are of interest because they can achieve significant speed-up relative to maximum likelihood approaches and comparable statistical efficiency.

We used Bayesian and maximum likelihood approaches to evaluate the effects of historical gene flow among populations.

Therefore, in this study several stochastic optimization methods are applied to solve parameter estimation problems for VLE modeling using both the classical least squares and maximum likelihood approaches.

For the estimation of parameters a penalized marginal likelihood approach is proposed which may be based on integration techniques like Gauss–Hermite quadrature but may as well be used within the more recently developed nonparametric maximum likelihood approaches.

We show that low-variation loci can be utilized in species-tree analyses that account for gene-tree uncertainty (e.g., a Bayesian framework), whereas maximum likelihood approaches show no improvement in accuracy when low-variation loci are added.

The NDA maximum likelihood approaches are undoubtedly the most accurate, but unfortunately, they are often computationally very expensive.

These conditions may cast doubt on statistical inferences about model parameters for count data with excess zero values based on the variance of the estimator under maximum likelihood approaches [25].

But in situations of short data snapshots, they may not be reliable and one would be obliged to trade low complexity for more accurate estimation by simply applying the maximum likelihood approaches.

Phylogenetic inference was performed using Bayesian and maximum-likelihood approaches.

Another set of successful techniques is based on maximum-likelihood approaches (Paninski et al. 2004).