Sentence examples for model selection allows from inspiring English sources

But in fact in our model, selection allows Wolbachia to be maintained at very high χ values because when the sib-mating rate χ is high, Wolbachia and the nuclear locus under selection segregate together.

Using ABC SMC for model selection allows us to estimate posterior model distributions correctly and demonstrate a considerable computational speed-up in ABC SMC compared with ABC rejection (Fig. 2).

Model selection allowed for inclusion of MGB-2 mRNA expression (selected in 96% of bootstrap samples) along with FIGO stage (selected in 88% of bootstrap samples).

Automatic model selection allowed us to detect amplicons for which all samples showed high (or low) counts since they were better modeled with a single Gaussian distribution.

In this study, we introduce a new and powerful set of methods for RNA model selection, allowing for the first time simple comparison between 4-state DNA models and their 7-state and 16-state RNA model counterparts.

An information-theoretic approach to model selection allowed us to address the importance of interactions between habitat variables, an aspect seldom considered in fragmentation studies, but which explained up to 65% of the variance in genetic parameters.

In the first set of models, the model M1a: Nearly Neutral allows 2 categories of codon sites in p0, and p1 proportions, with ω0 < 1, and ω1 = 1, whereas the model M2a: Selection allows an additional category of codons (p2) with ω2 > 1, indicating positive selection.

Logistic regression model On the basis of the cluster analyses and the selected eight X-variables two logistic regression models were used for Group 1 and Group 2, respectively, with the FORWARD (SLENTRY =0.1) stepwise model selection option allowing for the inclusion of main effects and two-way interaction terms.

Our model of selection allowed us to define a range of selective strengths that generated probabilities of having the + allele ranging from 0.5 to slightly less than 1, encompassing the entire relevant range.

A Bayesian model selection procedure allows direct comparison of different brain network models, determining which model of connectivity is most likely, given the data.

Model selection uncertainty allows for better parameter and variance estimates by using weighted averages of values from the set of best models [ 37, 46].