Overview
- Contains an integral view of possibility theory and its links to other uncertainty theories
- Includes possibilistic concepts for analytics and information fusion
- Contains applications to pattern recognition and other related fusion systems
Part of the book series: Information Fusion and Data Science (IFDS)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
- possibility distribution models
- imprecise type possibility distribution
- possibility and necessity measures
- possibilistic decision making
- marginal possibility distributions
- fuzzy measures and integrals
- possibilistic similarity measures
- possibilistic maximum likelihood
- data-driven science, modeling and theory building
About this book
The book discusses fundamental possibilistic concepts: distribution, necessity measure, possibility measure, joint distribution, conditioning, distances, similarity measures, possibilistic decisions, fuzzy sets, fuzzy measures and integrals, and finally, the interrelated theories of uncertainty..uncertainty. These topics form an essential tour of the mathematical tools needed for the latter chapters of the book. These chapters present applications related to decision-making and pattern recognition schemes, and finally, a concluding chapter on the use of possibility theory in the overall challenging design of an information fusion system. This book will appeal to researchers and professionals in the field of information fusion and analytics, information and knowledge processing, smart ICT, and decision support systems.
Authors and Affiliations
About the authors
Éloi Bossé, is a researcher on decision support, fusion of information and analytics technologies (FIAT). He possesses a vast research experience in applying them to Defense and Security related problems. He is currently president of Expertise Parafuse Inc., a consultant firm on FIAT, associate researcher at IMT-Atlantique, France. He holds a Ph.D. degree from Université Laval, Québec City, Canada.
Bibliographic Information
Book Title: Possibility Theory for the Design of Information Fusion Systems
Authors: Basel Solaiman, Éloi Bossé
Series Title: Information Fusion and Data Science
DOI: https://doi.org/10.1007/978-3-030-32853-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-32852-8Published: 27 December 2019
Softcover ISBN: 978-3-030-32855-9Published: 27 December 2020
eBook ISBN: 978-3-030-32853-5Published: 26 December 2019
Series ISSN: 2510-1528
Series E-ISSN: 2510-1536
Edition Number: 1
Number of Pages: X, 288
Number of Illustrations: 35 b/w illustrations, 87 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Probability and Statistics in Computer Science, Data-driven Science, Modeling and Theory Building, Communications Engineering, Networks