Probabilistic Risk Analysis

Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss 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 judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed.

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.