Sentence examples for model can adjust from inspiring English sources

The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity.

(Kulldorff [1997]) For example, the Poisson permutation model can adjust for multiple covariates within each cluster, thereby allowing practitioners to quantify how much impact in outcomes or in health services utilization is determined through geographic location.

Due to the high correlation of these factors with inflammation and malnutrition, adding them as proxies in the model can adjust the presence of inflammation and malnutrition partly.

All 6 of these models fit all 5 datasets well (Fig. 7), as one might expect since h not far from 1 implies that the data are not far from the Michaelis-Menten model that lives within each of these models if two parameters equal each, i.e. it is reasonable to expect that each model can adjust its 3rd parameter to meet differences between h = 1 and h = 1.1 to 1.3.

Kieffer: The business models can adjust here as well.

Regional modeling can adjust to this by treating the noise regionally; it also offers the possibility of constraining the inverse problem depending on how well the regional model parameters are resolved (e.g., Lesur and Maus, 2006).

Although an overt attack is less likely to occur, future models can adjust this assumption to account for a delay in diagnosis or outbreak detection.

Mixed linear models can adjust for such attrition to some degree because they include data from all participants, not just those who had observations made at every wave.

Because candidate models can adjust similarly to the data, we also considered functional redundancy, defined as the presence of both simple and complex models with similar response patterns [ 34].

Based on the developed growth model, we can adjust any coverage of graphene on the Cu foil which closely matches the fitting curve; the growth model predicts the growth rate of graphene.

In this study, we propose a supervised gradient-based learning model that can adjust its structure and parameters based on matching scores coming from many comparison functions (and applied to many fields), to efficiently classify the records.