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The mathematical description of the model uncertainty enables designing robust fault detection system, which effectiveness was verified with the DAMADICS benchmark.
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The proposed approach seems to offer better understanding of model uncertainties and enables the specification of their real characteristics.
Probabilistic sensitivity analysis (PSA) (1,000 iterations) was used to model joint parameter uncertainty, enabling cost-effectiveness acceptability curves (CEACs) to be calculated.
Because this study compares models, the choices regarding the parameter probability distribution functions are applied across models, which enables a more definitive ranking of model uncertainty.
This uncertainty modeling technique enables a compact and differentiable bound description of the parameter variations.
Sampling uncertainty and model uncertainty are two epistemic uncertainty sources.
The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective.
PLSR model uncertainty was estimated by jack-knifing33.
Vertical error bars indicate model uncertainty calculated by jack-knifing.
Consequently, model uncertainty inevitably arises.
Also known as model uncertainty.
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