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Engineering - Computational Intelligence and Complexity | Nonlinear Mathematics for Uncertainty and its Applications

Nonlinear Mathematics for Uncertainty and its Applications

Li, S., Wang, X., Okazaki, Y., Kawabe, J., Murofushi, T., Guan, L. (Eds.)

2011, XVIII, 710 p.

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  • Recent research on Nonlinear Mathematics and Uncertainty Scientific outcome of the conference "Non-Linear Mathematics
  • Uncertaity and Applications" held Sep. 7-9, 2011 in Bejing, China
  • Written by leading experts in the field

This volume is a collection of papers presented at the international conference on Nonlinear Mathematics for Uncertainty and Its Applications (NLMUA2011), held at Beijing University of Technology during the week of September 7--9, 2011. The conference brought together leading researchers and practitioners involved with all aspects of nonlinear mathematics for uncertainty and its applications.

Over the last fifty years there have been many attempts in extending the theory of classical probability and statistical models to the generalized one which can cope with problems of inference and decision making when the model-related information is scarce, vague, ambiguous, or incomplete. Such attempts include the study of nonadditive measures and their integrals, imprecise probabilities and random sets, and their applications in information sciences, economics, finance, insurance, engineering, and social sciences.

The book presents topics including nonadditive measures and nonlinear integrals, Choquet, Sugeno and other types of integrals, possibility theory, Dempster-Shafer theory, random sets, fuzzy random sets and related statistics, set-valued and fuzzy stochastic processes, imprecise probability theory and related statistical models, fuzzy mathematics, nonlinear functional analysis, information theory, mathematical finance and risk managements, decision making under various types of uncertainty, and others.

Content Level » Research

Keywords » Fuzzy Mathematics - Fuzzy Sets - Nonlinear Mathematics

Related subjects » Applications - Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

From the content: Ordinal Preference Models Based on S-Integrals and Their Verification.- Strong Laws of Large Numbers for Bernoulli Experiments under Ambiguity.- Comparative Risk Aversion for g-Expected Utility Maximizers.- Riesz Type Integral Representations for Comonotonically Additive Functionals.- Pseudo-Concave Integrals.- On Spaces of Bochner and Pettis Integrable Functions and Their Set-Valued Counterparts.- Upper Derivatives of Set Functions Represented as the Choquet Indefinite Integral.- On Regularity for Non-Additive Measure.

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