Mathematics of Uncertainty

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Focusing on the foundations of uncertainty in mathematics, this book explores key concepts such as variability, imprecision, and fuzziness in data. It covers a range of topics from interpolation and error propagation to fuzzy sets and neural networks. By addressing how observation fuzziness impacts the reliability of functional relationships, it delves into randomness and probability as models for measurement variability. Additionally, it introduces qualitative data analysis methods like cluster analysis, classification, and regression analysis, enriching the understanding of functional evaluations.