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Full Description
The book shows how risk, defined as the statistical expectation of loss, can be formally decomposed as the product of two terms: hazard probability and system vulnerability. This requires a specific definition of vulnerability that replaces the many fuzzy definitions abounding in the literature. The approach is expanded to more complex risk analysis with three components rather than two, and with various definitions of hazard. Equations are derived to quantify the uncertainty of each risk component and show how the approach relates to Bayesian decision theory. Intended for statisticians, environmental scientists and risk analysts interested in the theory and application of risk analysis, this book provides precise definitions, new theory, and many examples with full computer code. The approach is based on straightforward use of probability theory which brings rigour and clarity. Only a moderate knowledge and understanding of probability theory is expected from the reader.
Contents
- 1. Introduction to Probabilistic Risk Analysis (PRA). - 2. Distribution-Based Single-Threshold PRA. - 3. Sampling-Based Single-Threshold PRA. - 4. Sampling-Based Single-Threshold PRA: Uncertainty Quantification (UQ). - 5. Density Estimation to Move from Sampling- to Distribution-Based PRA. - 6. Copulas for Distribution-Based PRA. - 7. Bayesian Model-Based PRA. - 8. Sampling-Based Multi-Threshold PRA: Gaussian Linear Example. - 9. Distribution-Based Continuous PRA: Gaussian Linear Example. - 10. Categorical PRA with Other Splits than for Threshold-Levels: Spatio-Temporal Example. - 11. Three-Component PRA. - 12. Introduction to Bayesian Decision Theory (BDT). - 13. Implementation of BDT Using Bayesian Networks. - 14. A Spatial Example: Forestry in Scotland. - 15. Spatial BDT Using Model and Emulator. - 16. Linkages Between PRA and BDT. - 17. PRA vs. BDT in the Spatial Example. - 18. Three-Component PRA in the Spatial Example. - 19. Discussion.
