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Identification of Imprecision in Data Using $$\epsilon $$-Contamination Advanced Uncertainty Model
[chapter]
2021
Lecture Notes in Mechanical Engineering
AbstractOne of the importance of the contamination uncertainty model is to consider in-determinism in the uncertainty. We consider this advanced property and develop two methods. These methods identify if there is imprecision in a given model or data. In the first approach, we build two different—a probability distribution and an interval—models for a test function f via given data/model. Then, we identify the level of imprecision by assessing, so-called model trust, $$\epsilon \in (0,1)$$ ϵ ∈
doi:10.1007/978-3-030-77256-7_14
fatcat:rzuyoc2j65co7gszj6b2tth77a