TY - BOOK ID - 8434843 TI - Fuzzy Statistical Decision-Making : Theory and Applications AU - Kahraman, Cengiz. AU - Kabak, Özgür. PY - 2016 SN - 3319390120 3319390147 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Engineering. KW - Operations research. KW - Decision making. KW - Statistics. KW - Computational intelligence. KW - Computational Intelligence. KW - Statistical Theory and Methods. KW - Operation Research/Decision Theory. KW - Intelligence, Computational KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Deciding KW - Decision (Psychology) KW - Decision analysis KW - Decision processes KW - Making decisions KW - Management KW - Management decisions KW - Operational analysis KW - Operational research KW - Construction KW - Decision making KW - Artificial intelligence KW - Soft computing KW - Mathematics KW - Econometrics KW - Choice (Psychology) KW - Problem solving KW - Industrial engineering KW - Management science KW - Research KW - System theory KW - Industrial arts KW - Technology KW - Mathematical statistics. KW - Operations Research/Decision Theory. KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Fuzzy decision making. KW - Fuzzy mathematics KW - Statistics . UR - https://www.unicat.be/uniCat?func=search&query=sysid:8434843 AB - 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. ER -