Sentence examples for models with stronger from inspiring English sources
Dynamo models with stronger magnetic advection effects exhibit more distortion from the outer boundary to depth.
To conclude, adaptive Gaussian quadrature is a relevant alternative for the evaluation of MF for models with stronger nonlinearity, while being more computationally efficient than standard quadrature.
In addition, models with stronger LTM in the current climate tend to show larger increase in LTM in a warming climate, leading to an LTM control for change in the intensity of convective BL dehydration and resultant cloud feedback.
Ultimately, this may lead to development of psoriasis mouse models with stronger biochemical similarity to the human disease.
However, further characterization of each model at the biochemical level is needed for refinement and development of mouse models with stronger similarity to clinical disease [3] [5].
Indeed, recent empirical studies with gene expression data have shown that specific classifiers, such as Support Vector Machines (SVMs) produce models with stronger predictive ability (signal) and higher robustness across many high-throughput datasets compared to several widely-used alternatives [31] [33].
Parsimony is a criterion for evaluating models with strong theoretical support, and is consistent with the goal of finding the best model among a set of possible models.
We also note that the models with strong or partial strong MI restrictions which are of substantive interest in this study—fit the data considerably well.
Nevertheless, estimating the reliability of confidence estimates for relatively weak models is perhaps an even more critical need than making such estimates for models with strong performance statistics.
Under the dependence assumption of the errors, Berlinet et al. [6] proved the consistency of M-estimates for linear models with strong mixing errors.
The new method is contrasted with other alternatives and shown to provide robust models with strong predictive components across all latent variables.
