Sentence examples for models under test from inspiring English sources

Eight of the papers herein describe different models under test.

In these tests, scores obtained from observed earthquakes are compared with distributions of scores estimated from earthquakes expected from the models under test.

These changes can be tested in silico to determine those which will discriminate best between the models under test, before undertaking experiments.

Performing all calculations in silico before any experiments are undertaken is important in saving experimental time ensuring that experiments are only performed when the results will have discriminating property between the models under test.

A stepwise search algorithm, using both forward and backwards selection, was used to identify statistically significant factors using the Akaike Information Criterion (AIC) as a measure of the relative quality of a statistical models under test.

Aerodynamic balances are employed in wind tunnels to estimate the forces and moments acting on the model under test.

The results show that the means of L-scores for earthquakes conforming to a model under test and for those with parameter uncertainties taken into consideration are similar whether computed by our method or by the method involving simulated catalogs.

To compare our procedure with the current one based on the Monte Carlo method, we randomly generated sets of 10,000 earthquake catalogs of two kinds: one set conforming to the model under test, and the other derived from the observed catalog allowing for uncertainties in magnitude and hypocentral parameters.

However, this additional capability also presents technical challenges: waves generated on "one side" of the tank must be absorbed on the opposite side, together with any waves reflected or radiated by the model under test, to prevent contamination of the wave field.

Comparative fit index (CFI) compares the fit of the model under test with a model in which none of the variables are related.

Finally, the test data corresponding to the selected channel and feature subset for the trained model under test was used to calculate the test error rate.