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

Olga Kosheleva

ISBN 10: 303031040X / ISBN 13: 9783030310400

Published by Springer International Publishing, 2020

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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, suitablefor graduate students.

Seller Inventory # 9783030310400

Bibliographic Details

• Title: Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications
• Publisher: Springer International Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 303031040X
• ISBN 13: 9783030310400
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 41D millimeters 241 millimeters width by 160 millimeters height by 41 millimeters depth
• Weight: 1150 grams

About this title

Synopsis

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, suitablefor graduate students.