Sentence examples for likelihood approaches from inspiring English sources

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

For low-dimensional problems, classical deviance tests are also included and compared with penalized likelihood approaches.

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.

However, similar to hardware failures, traditionally human error has been quantified using likelihood approaches; this viewpoint abnegates the role of the cognitive abilities of the operators.

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.

In contrast, OSEM with a low number of iterations or standard penalised likelihood approaches produce images with a non-uniform spatial resolution.

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].