Advances in the Theory of Probabilistic and Fuzzy Data Scientific Methods with Applications (Studies in Computational Intelligence)

Dombi, J�zsef; J�n�s, Tam�s

ISBN 10: 3030519511 ISBN 13: 9783030519513

Editorial: Springer, 2021

Idioma: Ingl�s

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Detalles bibliogr�ficos

T�tulo

Advances in the Theory of Probabilistic and Fuzzy Data Scientific Methods with Applications (Studies in Computational Intelligence)

Editor

Springer

Fecha de publicaci�n

2021

Idioma

Ingl�s

ISBN 10

3030519511

ISBN 13

9783030519513

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

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Sinopsis

This book focuses on the advanced soft computational and probabilistic methods that the authors have published over the past few years. It describes theoretical results and applications, and discusses how various uncertainty measures - probability, plausibility and belief measures - can be treated in a uniﬁed way. It also examines approximations of four notable probability distributions (Weibull, exponential, logistic and normal) using a uniﬁed probability distribution function, and presents a fuzzy arithmetic-based time series model that provides an easy-to-use forecasting technique. Lastly, it proposes flexible fuzzy numbers for Likert scale-based evaluations. Featuring methods that can be successfully applied in a variety of areas, including engineering, economics, biology and the medical sciences, the book offers useful guidelines for practitioners and researchers.

De la contraportada

This book focuses on the advanced soft computational and probabilistic methods that the authors have published over the past few years. It describes theoretical results and applications, and discusses how various uncertainty measures – probability, plausibility and belief measures – can be treated in a uni?ed way. It also examines approximations of four notable probability distributions (Weibull, exponential, logistic and normal) using a uni?ed probability distribution function, and presents a fuzzy arithmetic-based time series model that provides an easy-to-use forecasting technique. Lastly, it proposes flexible fuzzy numbers for Likert scale-based evaluations. Featuring methods that can be successfully applied in a variety of areas, including engineering, economics, biology and the medical sciences, the book offers useful guidelines for practitioners and researchers.

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