Sentence examples for model discrimination criterion from inspiring English sources

To address these issues, a systematic, incremental strategy for estimating the free flux parameters and knot locations has been devised, based on the Akaike model discrimination criterion (AIC).

We rely on formal model discrimination criteria for a quantitative evaluation of the interpretive skill of each of the candidate models tested.

The capabilities of model-robust supersaturated designs for model discrimination are assessed using a model-discrimination criterion, the subspace angle.

The best designs according to the proposed model-discrimination criteria are obtained and tabulated for practical use.

Based on the inverse modeling results, the seven sorption-process models are discriminated using four model discrimination (or selection) criteria, Akaike information criterion (AIC), modified Akaike information criterion (AICc), Bayesian information criterion (BIC) and Kashyap information criterion (KIC).

Here, we present the model overlap as a robust discrimination criterion, measuring dissimilarities of model response PDFs used to rate the discriminative power of a design during optimization.

We introduce new criteria for model discrimination and use these and existing criteria to evaluate standard orthogonal designs.

The HBMA result shows that each highly probable proposition can be identified for each uncertain model component once the discrimination criterion is achieved.

Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data.

This information is used to give criteria for model discrimination and parameter estimation based on simple experimental paradigms.

To evaluate model discrimination performance, we used diagnostic criteria such as sensitivity, specificity, and the area under the receiver operating characteristics curves (AUC).