Sentence examples for models we consider the from inspiring English sources

In order to compare the models, we consider the following goodness-of-fit statistics: Akaike information criterion (AIC) and Kolmogorov-Smirnov (K-S) measure with the associated p-value.

In this paper we extend this result in two directions: first, in contrast with aggregated models, we consider the more natural and widely popular structure preserving models (SPM) that preserve the identity of the network components and allow for a more realistic treatment of the loads.

For the simulation of both models, we consider the same network layout as shown in Fig. 3 and simulation parameters as given in Table 2. Our network comprises 50 UEs per cell and an antenna port configuration of N Tx×N Rx=4×2.

In both models we consider the effects of antivirals and/or vaccination that are available during that period.

Only if this is almost equally well reproduced by two models, we consider the overall spike rate, i.e., the spiking frequency within bursts, as a secondary criterion - hence the 10 fold less weight for the integral of the SDF.

Before turning to these experimental models, we consider the main cell types and behaviours involved in tumour progression.

In the mixed-effects models, we considered the gilz mRNA expression logarithm in order to improve data normality.

To reduce complexities arising due to the BMI dynamics while building the models, we considered the BMI measurements made at the time of hypertension diagnosis in our analysis.

In the multiple regression models, we considered the effect of age, gender, location, lesion type and oral risk factors on malignant transformation.

In the multiple regression models, we considered the effect of age of diagnosis, gender, subsite location, lesion type and lifestyle habits on malignant transformation.

As a model example, we consider the following result of Mustafa et al. [8].