Fuzzy Statistical Decision-Making Theory and Applications / [electronic resource] :
edited by Cengiz Kahraman, Özgür Kabak.
- 1st ed. 2016.
- XII, 356 p. 84 illus., 5 illus. in color. online resource.
- Studies in Fuzziness and Soft Computing, 343 1860-0808 ; .
- Studies in Fuzziness and Soft Computing, 343 .
This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.
9783319390147
10.1007/978-3-319-39014-7 doi
Computational intelligence.
Statistics .
Operations research.
Computational Intelligence.
Statistical Theory and Methods.
Operations Research and Decision Theory.
Q342
006.3
This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.
9783319390147
10.1007/978-3-319-39014-7 doi
Computational intelligence.
Statistics .
Operations research.
Computational Intelligence.
Statistical Theory and Methods.
Operations Research and Decision Theory.
Q342
006.3