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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.
| Autorius: | Tamás Jónás, József Dombi, |
| Serija: | Studies in Computational Intelligence |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 208 |
| ISBN-10: | 3030519511 |
| ISBN-13: | 9783030519513 |
| Formatas: | 235 x 155 x 12 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Advances in the Theory of Probabilistic and Fuzzy Data Scientific Methods with Applications“
