Sentence examples for model predictions when from inspiring English sources
A posteriori probabilities could be used to increase confidence in the model predictions: when hospitals were correctly predicted, a posteriori probabilities were always close to 1, whereas when hospitals were misclassified, a posteriori probabilities were mostly closer to 0.5.
The new method substantially improves the model predictions when compared with traditional least square regression methods.
Good agreement is demonstrated between experimental measurements and model predictions when relying on material parameter values given in literature.
The proposed model is observed to provide satisfactory model predictions when compared to other models available in literature.
The two effective glass transition temperatures vary with composition, in agreement with the self-concentration model predictions when using the Kuhn's segment length for each component.
The experimental levels of reinforcement appear to be reasonably consistent with model predictions when changes in particle aspect ratios are accounted for.
In Figure 6B, actual performance was compared to the model prediction when the second target was five times more rewarding than the first.
For a computational/system biologist, this shows that it is essential to further analyze a model prediction, when results are obtained from reverse engineering based on parameter estimation, since some of its properties may or may not invalidate the results.
Using a common approach for constructing dynamic models for predicting Clostridium perfringens growth in ready-to-eat uncured beef during cooling, there was no appreciable difference between the models' predictions when the population of cells was within the lag or exponential phases of growth.
It is found that all of the models perform nearly identical for cases with a low evaporation rate, while significant deviations among the model predictions emerge when the evaporation rate is increased.
MAPE has been shown to be a very effective measure of error in model predictions, especially when considering population forecasting [39, 41].
