Sentence examples for model can strongly from inspiring English sources

The choice of an unsuitable molecular clock model can strongly bias divergence time estimates.

In addition, the fidelity of the model can strongly influence whether or not the model can capture the behavior observed in the data, making it possible that one or more than one minima exist.

Adding evolutionary dynamics to metacommunity models can strongly alter patterns of species richness and abundance, food web structure, community assembly dynamics, and ecosystem properties (Urban 2006; Vellend 2006; Rossberg et al. 2008; Loeuille 2010).

For true audio signals, however, the L model will perform worse in terms of perceptual frequency resolution since the estimated model parameters can strongly differ for noise-free and noisy sinusoidal signals, see Figures 2(a) and 3(a).

If a Poisson or negative binomial distribution based count model is applied to data with an excess of zeros without addressing these mixtures, the model can be strongly affected.

Instrumental and model uncertainty contributions can strongly affect the performance of the ROC analysis especially in the evaluation of performance metrics such as Area Under ROC (AUC) and Optimal Operating Points.

Model comparisons and hypothesis tests indicate that (1) boundedly rational behavior is prevalent in initial-period play, (2) homogeneous population models can be strongly rejected in favor of heterogeneous population models, and (3) deductive selection principles add no statistically significant contribution to explaining the data.

Predictions from such incomplete models can be strongly biased and if used uncritically predictions can be misleading.

Moreover, the AIC values for BAYAREALIKE + J are 18.7 (relaxed) and 23 (harsh) units lower than the next best supported model (i.e., DEC+J), which suggests that the former model can be regarded as strongly outperforming the other five models18.

Growth models in stock assessments can strongly influence the estimated biomass that affect the conclusion of stock status and exploitation level.

In building the KNN model the choice of k can strongly influence the quality of predictions: a small value of k leads to a large variance in predictions; alternatively, setting k to a large value may lead to a large model bias since the k nearest neighbours are farther away including cases that are less representative of the case under examination.