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Statistical methods for fuzzy data /
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be b...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex :
Wiley,
2011.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Viertl, R. |q (Reinhard) | |
| 245 | 1 | 0 | |a Statistical methods for fuzzy data / |c Reinhard Viertl. |
| 260 | |a Chichester, West Sussex : |b Wiley, |c 2011. | ||
| 300 | |a 1 online resource (xii, 256 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references (pages 251-252) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Statistical Methods forFuzzy Data; Contents; Preface; Part I FUZZY INFORMATION; 1 Fuzzy data; 1.1 One-dimensional fuzzy data; 1.2 Vector-valued fuzzy data; 1.3 Fuzziness and variability; 1.4 Fuzziness and errors; 1.5 Problems; 2 Fuzzy numbers and fuzzy vectors; 2.1 Fuzzy numbers and characterizing functions; 2.2 Vectors of fuzzy numbers and fuzzy vectors; 2.3 Triangular norms; 2.4 Problems; 3 Mathematical operations for fuzzy quantities; 3.1 Functions of fuzzy variables; 3.2 Addition of fuzzy numbers; 3.3 Multiplication of fuzzy numbers; 3.4 Mean value of fuzzy numbers. | |
| 520 | |a Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Fuzzy measure theory. | |
| 650 | 0 | |a Fuzzy sets. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 6 | |a Mesures floues (Mathématiques) | |
| 650 | 6 | |a Ensembles flous. | |
| 650 | 7 | |a MATHEMATICS |x Calculus. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Mathematical Analysis. |2 bisacsh | |
| 650 | 7 | |a Fuzzy measure theory. |2 fast |0 (OCoLC)fst00936810 | |
| 650 | 7 | |a Fuzzy sets. |2 fast |0 (OCoLC)fst00936812 | |
| 650 | 7 | |a Mathematical statistics. |2 fast |0 (OCoLC)fst01012127 | |
| 776 | 0 | 8 | |i Print version: |a Viertl, R. (Reinhard). |t Statistical methods for fuzzy data. |d Chichester, West Sussex : Wiley, 2011 |z 9780470699454 |w (DLC) 2010031105 |w (OCoLC)246887089 |
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