Building Bridges between Soft and Statistical Methodologies for Data Science /

Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, co...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: García-Escudero, Luis A. (Editor), Gordaliza, Alfonso (Editor), Mayo, Agustín (Editor), Lubiano Gomez, María Asunción (Editor), Gil, Maria Angeles (Editor), Grzegorzewski, Przemyslaw (Editor), Hryniewicz, Olgierd (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2023.
Edition:1st ed. 2023.
Series:Advances in Intelligent Systems and Computing, 1433
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary:Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, computer scientists, engineers, econometricians, and psychologists are more and more interested in facing this challenge. As a consequence, there is a clear need to build bridges between all these communities for Data Science. This book contains more than fifty selected recent contributions aiming to establish the above referred bridges. These contributions address very different and relevant aspects such as imprecise probabilities, information theory, random sets and random fuzzy sets, belief functions, possibility theory, dependence modelling and copulas, clustering, depth concepts, dimensionality reduction of complex data and robustness.
Physical Description:1 online resource (XIII, 408 pages 83 illustrations, 55 illustrations in color)
ISBN:9783031155093
ISSN:2194-5365 ;
DOI:10.1007/978-3-031-15509-3