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But for all models, even for the least robust Models 1 and 2, robustness increases with increasing Hill exponent, and is always smaller than CVuni.
Use of statistical methods investigates how the selected critical parameters feature highly robust models, thereby linking reactions to robustness.
First with community "assets" – individuals and groups with untapped "social capital" – and secondly with the wider public via more robust models of representative and participatory engagement.
It picks the most robust models and discards the others.
Prediction requires robust models that can simulate biological systems.
Therefore, we investigated how to develop robust models.
Second, we require only a modest number of training examples (10 15) to learn robust models.
Therefore, observatory data had to be used to obtain robust models.
Such applications include many parameters and require robust models from which observations can then be compared.
(Pop and Sârbu 1996) proposed a new fuzzy regression algorithm to obtain robust models.
Outer approximation (OA) and Benders' decomposition (BD) are used to solve the four robust models.
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