Sentence examples for model output more from inspiring English sources

Copula function can conquer this shortcoming, the wind speed model considering dependence makes wind turbine model output more concentrated which will cut off the calculating time and insure the precision at the same time.

However, because the primary purpose of the model is to illustrate the effects of travel coupling between the US and Canada rather than to exactly predict future incidence, the introduction of extraneous processes corresponding to the additional parameters may make the model output more difficult to interpret.

More importantly, the global sensitivity analysis reveals that for time periods greater than a few thousand years, the uncertainty of the model output is more sensitive to the values of the individual parameters than to the interaction between them.

By incorporating genomic positions into the sojourn distribution of HSMM, with optional prior learning using annotation or previous studies, model output is more biologically sensible.

Source data for Figure 4. DOI: http://dx.doi.org/10.7554/eLife.00288.022 10.7554/eLiFigure88.023 Figure 4 figure supplement 1. Continuous sampling of spatial model output reveals more accurate measures of episode characteristics.

Sobol' [ 9, 10] introduced so-called global sensitivity indices that describe the impact of specific parameters or combinations thereof on the uncertainty in the model output and more in particular on the variance of the output distribution, hence the term 'variance-based' GSA.

As a result, interpretations of model outputs were more measured and cautious.

We also performed scenario sensitivity analyses in which multiple parameters changed simultaneously to test model outputs under more extreme conditions.

The N-way HC-PLSR method presented here provides the opportunity to improve both prediction accuracy and analytical insight by identification of regional subsets of the data within which the relationships between input parameters and model outputs are more transparent than in a global regression analysis.

The interpretation of AUC values is consistent with those generated in other contexts, with possible values ranging between 0 and 1, and values greater than 0.50 indicative of model output that is more useful than randomly generated risk scores.

We used these ranges for mutation rate increase and DFE variance over lifetime as the X and Y axes of a plane, with the Z axis represented by the model output of interest (more details on generating the surface plots can be found in Supplemental Methods).