Sentence examples for common likelihood from inspiring English sources

Figure 2 Three common likelihood combination operators, normalized sum ( -norm), product ( -norm), and Hamacher -norm are illustrated along their resulting likelihoods.

As in all common likelihood-based methods, PROCOV implements a pruning algorithm [ 36] for the likelihood calculation.

Here we treat only the most common maximum likelihood and Bayesian methods.

This approach outputs the common high likelihood areas of the measurements, and so the unnecessary peaks of the SLF are somewhat reduced.

Similarly, [50] proposed another additive structure with multiple decision trees, but a minimum generation error (MGE) measure was used as the selection criterion instead of the more common maximum likelihood (ML) measure.

An alternative approach that can be applied consists in employing adaptation techniques, such as the common Maximum Likelihood Linear Regression (MLLR and MAPP techniques, which have been shown to improve recognition performance [20, 21, 23, 26, 28].

To this end, we estimated women's paid work and fertility jointly in a common maximum likelihood framework.

These family classes have generic definitions, which imply that a common maximum likelihood method for estimating the parameters of the model can be applied to all of them.

By estimating fertility and women's employment jointly in a common maximum likelihood framework we showed that a failure to account for the unmeasured time-constant unobserved characteristics of women would lead to an underestimation of the negative impact of women's employment on first and second birth risks.

The stochastic origin ensembles (SOE) method was investigated as an imaging method for the DPI because only a minimal estimation of system response is required to produce images with quality that is comparable to common maximum-likelihood methods.

Approximate Bayesian and summary statistic methods including multiple summary statistics (e.g. both temporal F and LD) could greatly improve precision and accuracy of N e estimators (Tallmon et al. 2008; Luikart et al. 2010), especially as large population genomic data sets become common making likelihood-based methods even computationally demanding to evaluate (Luikart et al. 2003).