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Furthermore the results of static and dynamic methods of measurement of model parameters were compared.
Prequential Evaluation is available as well, which enables measurement of model accuracy, either from the start or based on a sliding window of recent instances.
In particular, a precise measurement of model characteristics can be beneficial from a business perspective, helping to save costs thanks to early error detection.
Peak value of 152 pT for the measurement of model half-cylinder sample and that of 6.6 pT for the theoretical curve suggest that the total magnetic moment for the measurement is estimated to be 3.0 × 10−8 A m2.
However, the measurement of Model B was coarse due to categorization.
Akaike's information criterion (AIC) was used to determine a primary measurement of model fit; a lower value of AIC indicated a better model fit.
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These variables also estimate the relationship between factors and factors structure, and allow the measurement of models with more flexibility.
Over-representation of some sequence families (e.g. calcium dependent protein kinases) relative to others in the sequence sets used for algorithm testing could potentially lead to biased measurements of model performance.
This enables a comparison of a single measurement value of model precision (D-criterion), from different study designs, to the costs for these designs.
The inter-compartmental resistance is quantified by inversely proportional model parameter, named as inter-compartmental mass transfer coefficient; however, direct measurement of this model parameter is not possible.
The relative error provides an overall measurement of the model performance.
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Justyna Jupowicz-Kozak
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