Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy Etc.: Methods and Their Applications

Kosheleva, Olga (Edited by)/ Shary, Sergey P. (Edited by)/ Xiang, Gang (Edited by)/ Zapatrin, Roman (Edited by)

ISBN 10: 303031040X / ISBN 13: 9783030310400

Editore: Springer, 2020

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660 pages. 9.25x6.10x1.44 inches. In Stock.

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Dati bibliografici

• Titolo: Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy Etc.: Methods and Their Applications
• Editore: Springer
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 303031040X
• ISBN 13: 9783030310400
• Rilegatura: Hardcover
• Condizione: Brand New

Descrizione articolo

Riassunto

Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. 

In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.