Measuring Uncertainty within the Theory of Evidence, Simona Salicone; Marco Prioli

Автор: Morrison, Faith A.
Название: Uncertainty analysis for engineers and scientists
ISBN: 1108745741 ISBN-13(EAN): 9781108745741
Издательство: Cambridge Academ
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Цена: 6970.00 р.
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Описание: Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Whether you are new to the sciences or an experienced engineer, this useful text provides a practical approach to performing error analysis.

Автор: Todinov, Michael T.
Название: Risk and Uncertainty Reduction by Using Algebraic Inequalities
ISBN: 0367898004 ISBN-13(EAN): 9780367898007
Издательство: Taylor&Francis
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Цена: 17609.00 р.
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Описание: This book provides the reader with a domain-independent method for reducing risk through maximizing reliability, reducing epistemic uncertainty, reducing aleatory uncertainty, ranking the reliabilities of systems and processes, minimizing the risk of faulty assemblies, and ranking decision-making alternatives in the presence of deep uncertainty.

Автор: Marcot, Bruce G.
Название: The Science and management of uncertainty
ISBN: 036763340X ISBN-13(EAN): 9780367633400
Издательство: Taylor&Francis
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Цена: 16843.00 р.
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Описание: This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management.

Автор: Salicone, Simona Prioli, Marco
Название: Measuring uncertainty within the theory of evidence
ISBN: 3319741373 ISBN-13(EAN): 9783319741376
Издательство: Springer
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Цена: 15372.00 р.
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Описание: 1. Introduction.- Part I: The background of the Measurement Uncertainty.- 2. Measurements.- 3. Mathematical Methods to handle Measurement Uncertainty.- 4. A first, preliminary example.- Part II: The mathematical Theory of the Evidence.- 5. Introduction: probability and belief functions.- 6. Basic definitions of the Theory of Evidence.- 7. Particular cases of the Theory of Evidence.- 8. Operators between possibility distributions.- 9. The joint possibility distributions.- 10. The combination of the possibility distributions.- 11. The comparison of the possibility distributions.- 12. The Probability-Possibility Transformations.- Part III: The Fuzzy Set Theory and the Theory of the Evidence.- 13. A short review of the Fuzzy Set Theory.- 14. The relationship between the Fuzzy Set Theory and the Theory of Evidence.- Part IV: Measurement Uncertainty within the mathematical framework of the Theory of the Evidence.- 15. Introduction: towards an alternative representation of the Measurement Results.- 16. Random-Fuzzy Variables and Measurement Results.- 17. The Joint Random-Fuzzy variables.- 18. The Combination of the Random-Fuzzy Variables.- 19. The Comparison of the Random-Fuzzy Variables.- 20. Measurement Uncertainty within Fuzzy Inference Systems.- Part V: Application examples.- 21. Phantom Power measurement.- 22. Characterization of a resistive voltage divider.- 23. Temperature measurement update.- 24. The Inverted Pendulum.- 25. Conclusion.- References.- Index.

Автор: Prasanta S. Bandyopadhyay; Gordon Brittan Jr.; Mar
Название: Belief, Evidence, and Uncertainty
ISBN: 3319277707 ISBN-13(EAN): 9783319277707
Издательство: Springer
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Цена: 6986.00 р.
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Описание: Chapter 1. Introduction.- Chapter 2. Confirmation and Evidence.- Chapter 3. Confirmation, Evidence and Inference.- Chapter 4. Subjective Bayesian Accounts of Evidence.- Chapter 5. Mapping the Concept of Misleading Evidence.- Chapter 6. Non-Bayesian Accounts of Evidence.- Chapter 7. Error-Statistics and Severe Testing.- Chapter 8. Confirmation/Evidence Epistemology and Resolution of Paradoxes.- Chapter 9. Concluding Remarks.

Автор: Gilboa
Название: Theory of Decision under Uncertainty
ISBN: 052151732X ISBN-13(EAN): 9780521517324
Издательство: Cambridge Academ
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Описание: This book describes classical axiomatic theories of decision under uncertainty, critiques thereof, and alternative theories. It discusses the meaning of probability, focusing on the behavioral definition of subjective probability by Savage`s theorem. It presents non-additive and multiple prior theories, as well as the case-based approach to the formation of beliefs.