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Uncertainty Theory

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When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case.

In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees.

This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, control, and finance.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Berlin, Heidelberg., 2015

Collation

105 b/w illustrations

Language

English

ISBN/ISSN

978-3-662-44354-5

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Computational Intelligence
Probability Theory
Operations Research and Decision Theory
Probability and Statistics in Computer Science

Specific Detail Info

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Statement of Responsibility

Baoding Liu

Other Information

Cataloger

Yudi

Source

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