Probabilistic Risk Analysis: Foundations and Methods

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Probabilistic risk analysis aims to quantify the risk caused by high technology installations in situations where classical statistical analysis is difficult or impossible. This book discusses the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgment and uncertainty propagation.

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Editorial Reviews

From the Publisher
"This is a valuable reference book for engineers, researchers and Ph.D. students...The book is fresh, neat, and comprehensive. I highly recommend it." Risk Decision and Policy

" excellent basis for an undergraduate course, well equipped with a wealth of real-life examples and exercises. Practitioners and decision makers will also benefit...The text can be recommended as an important contribution to bridge the gap between those working in mathematical reliability, who are looking for real-life examples, and those in eonomic risk analysis, who are looking for a readable treatment of the foundational concepts." JASA

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Product Details

  • ISBN-13: 9780521773201
  • Publisher: Cambridge University Press
  • Publication date: 6/27/2001
  • Edition description: New Edition
  • Pages: 414
  • Product dimensions: 6.85 (w) x 9.72 (h) x 1.02 (d)

Table of Contents

Part I. Introduction: 1. Probabilistic risk analysis; Part II. Theoretical Issues and Background: 2. What is uncertainty?; 3. Probabilistic methods; 4. Statistical inference; 5. Weibull analysis; Part II. System Analysis and Quantification: 6. Fault and event trees; 7. Fault trees - analysis; 8. Dependent failures; 9. Reliability data bases; 10. Expert opinion; 11. Human reliability; 12. Software reliability; Part IV. Uncertainty Modeling and Risk Measurement: 13. Decision theory; 14. Influence diagrams and belief nets; 15. Project risk management; 16. Probabilistic inversion; 17. Uncertainty analysis; 18. Risk measurement and regulation; Bibliography; Index.

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