Sentence examples for model evaluations are from inspiring English sources

Since model evaluations are computationally intensive, instead of traditional Monte Carlo methods, we rely on polynomial chaos (PC) expansions for representation of the stochastic model response.

In predicting future tsunami scenarios, post-event model evaluations are not possible and a priori performance tests of individual models are not always feasible to perform.

Threshold-based model evaluations are highly dependent on the selected threshold and the method to select the cutoff value c.

More precisely, in a MC simulation multiple model evaluations are performed using random numbers to sample from probabilistic distributions of model inputs.

The model evaluations were performed using observed streamflows, GRACE-derived TWSC, and actual evapotranspiration (ET) estimates from flux towers and from the water balance approach.

Nevertheless, one common problem in Bayesian methods is the considerably high computational cost since a large number of model evaluations is required in the likelihood evaluation.

Model evaluations were based on Χ2 statistics that measure the discrepancy between observed and model-implied covariance matrices.

The reverse-engineering process terminates, when the training error drops below a pre-defined error threshold, or when a maximum number of model evaluations is reached.

Model evaluations were carried out on the basis of the following goodness-of-fit indices: the comparative fit index and the root mean square error of approximation.

Model evaluations were carried out using three 'goodness of fit' indices: χ2 is reported as an absolute fit index, while Bentler's comparative fit index (CFI) and Root Mean Square Error of Approximation (RMSEA) are reported as comparative indices.

Results from the PLSA model training and model evaluation are presented in Tables 2, 3, and 4 and Figure 7.