Uncertainty Modeling and Analysis in Engineering and the Sciences

Uncertainty Modeling and Analysis in Engineering and the Sciences

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Engineers and scientists often need to solve complex problems with incomplete information resources, necessitating a proper treatment of uncertainty and a reliance on expert opinions. Uncertainty Modeling and Analysis in Engineering and the Sciences prepares current and future analysts and practitioners to understand the fundamentals of knowledge and ignorance, how to model and analyze uncertainty, and how to select appropriate analytical tools for particular problems. This volume covers primary components of ignorance and their impact on practice and decision making. It provides an overview of the current state of uncertainty modeling and analysis, and reviews emerging theories while emphasizing practical applications in science and engineering. The book introduces fundamental concepts of classical, fuzzy, and rough sets, probability, Bayesian methods, interval analysis, fuzzy arithmetic, interval probabilities, evidence theory, open-world models, sequences, and possibility theory. The authors present these methods to meet the needs of practitioners in many fields, emphasizing the practical use, limitations, advantages, and disadvantages of the methods.show more

Product details

  • Hardback | 400 pages
  • 157.5 x 236.2 x 27.9mm | 703.08g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 99 black & white illustrations, 79 black & white tables
  • 1584886447
  • 9781584886440
  • 1,888,045

Table of contents

Systems, Knowledge, and Ignorance Data Abundance and Uncertainty Systems Framework Knowledge Ignorance From Data to Knowledge for Decision Making Encoding Data and Expressing Information Introduction Identification and Classification of Theories Crisp Sets and Operations Fuzzy Sets and Operations Generalized Measures Rough Sets and Operations Gray Systems and Operations Uncertainty and Information Synthesis Synthesis for a Goal Knowledge, Systems, Uncertainty, and Information Measure Theory and Classical Measures Monotone Measures and Their Classification Dempster-Shafer Evidence Theory Possibility Theory Probability Theory Imprecise Probabilities Fuzzy Measures and Fuzzy Integrals Uncertainty Measures Introduction Uncertainty Measures: Definition and Types Nonspecificity Measures Entropy-Like Measures Fuzziness Measure Application: Combining Expert Opinions Uncertainty-Based Principles and Knowledge Construction Introduction Construction of Knowledge Minimum Uncertainty Principle Maximum Uncertainty Principle Uncertainty Invariance Principle Methods for Open-World Analysis Uncertainty Propagation for Systems Introduction Fundamental Methods for Propagating Uncertainty Propagation of Mixed Uncertainty Types Expert Opinions and Elicitation Methods Introduction Terminology Classification of Issues, Study Levels, Experts, and Process Outcomes Process Definition Need Identification for Expert Opinion Elicitation Selection of Study Level and Study Leader Selection of Peer Reviewers and Experts Identification, Selection, and Development of Technical Issues Elicitation of Opinions Documentation and Communication Visualization of Uncertainty Introduction Visualization Methods Criteria and Metrics for Assessing Visualization Methods Intelligent Agents for Icon Selection, Display, and Updating Ignorance Markup Language Appendix A: Historical Perspectives on Knowledgeshow more

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