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Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods

Book Description

Explores methods for the representation and treatment of uncertainty in risk assessment

In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications.

While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences.

Uncertainty in Risk Assessment:

  • Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts.

  • Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory.

  • Offers guidance on when to use probability and when to use an alternative representation of uncertainty.

  • Presents and discusses methods for the representation and characterization of uncertainty in risk assessment.

  • Uses examples to clearly illustrate ideas and concepts.

  • Table of Contents

    1. Cover
    2. Title Page
    3. Copyright
    4. Preface
    5. Part I: Introduction
      1. Chapter 1: Introduction
        1. 1.1 Risk
        2. 1.2 Probabilistic Risk Assessment
        3. 1.3 Use of Risk Assessment: The Risk Management and Decision-Making Context
        4. 1.4 Treatment of Uncertainties in Risk Assessments
        5. 1.5 Challenges: Discussion
      2. References – Part I
    6. Part II: Methods
      1. Chapter 2: Probabilistic Approaches for Treating Uncertainty
        1. 2.1 Classical Probabilities
        2. 2.2 Frequentist Probabilities
        3. 2.3 Subjective Probabilities
        4. 2.4 The Bayesian Subjective Probability Framework
        5. 2.5 Logical Probabilities
      2. Chapter 3: Imprecise Probabilities for Treating Uncertainty
      3. Chapter 4: Possibility Theory for Treating Uncertainty
        1. 4.1 Basics of Possibility Theory
        2. 4.2 Approaches for Constructing Possibility Distributions
      4. Chapter 5: Evidence Theory for Treating Uncertainty
      5. Chapter 6: Methods of Uncertainty Propagation
        1. 6.1 Level 1 Uncertainty Propagation Setting
        2. 6.2 Level 2 Uncertainty Propagation Setting
      6. Chapter 7: Discussion
        1. 7.1 Probabilistic Analysis
        2. 7.2 Lower and Upper Probabilities
        3. 7.3 Non-Probabilistic Representations with Interpretations Other Than Lower and Upper Probabilities
        4. 7.4 Hybrid Representations of Uncertainty
        5. 7.5 Semi-Quantitative Approaches
      7. References – Part II
    7. Part III: Practical Applications
      1. Chapter 8: Uncertainty Representation and Propagation in Structural Reliability Analysis
        1. 8.1 Structural Reliability Analysis
        2. 8.2 Case Study
        3. 8.3 Uncertainty Representation
        4. 8.4 Uncertainty Propagation
        5. 8.5 Results
        6. 8.6 Comparison to a Purely Probabilistic Method
      2. Chapter 9: Uncertainty Representation and Propagation in Maintenance Performance Assessment
        1. 9.1 Maintenance Performance Assessment
        2. 9.2 Case Study
        3. 9.3 Uncertainty Representation
        4. 9.4 Uncertainty Propagation
        5. 9.5 Results
      3. Chapter 10: Uncertainty Representation and Propagation in Event Tree Analysis
        1. 10.1 Event Tree Analysis
        2. 10.2 Case Study
        3. 10.3 Uncertainty Representation
        4. 10.4 Uncertainty Propagation
        5. 10.5 Results
        6. 10.6 Comparison of the Results to Those Obtained by Using Other Uncertainty Representation and Propagation Methods
        7. 10.7 Result Comparison
      4. Chapter 11: Uncertainty Representation and Propagation in the Evaluation of the Consequences of Industrial Activity
        1. 11.1 Evaluation of the Consequences of Undesirable Events
        2. 11.2 Case Study
        3. 11.3 Uncertainty Representation
        4. 11.4 Uncertainty Propagation
        5. 11.5 Results
        6. 11.6 Comparison of the Results to Those Obtained Using a Purely Probabilistic Approach
      5. Chapter 12: Uncertainty Representation and Propagation in the Risk Assessment of a Process Plant
        1. 12.1 Introduction
        2. 12.2 Case Description
        3. 12.3 The “Textbook” Bayesian Approach (Level 2 Analysis)
        4. 12.4 An Alternative Approach Based on Subjective Probabilities (Level 1 Analysis)
      6. References – Part III
    8. Part IV: Conclusions
      1. Chapter 13: Conclusions
      2. References – Part IV
    9. Appendix A: Operative Procedures for the Methods of Uncertainty Propagation
      1. A.1 Level 1 Hybrid Probabilistic–Possibilistic Framework
      2. A.2 Level 2 Purely Probabilistic Framework
    10. Appendix B: Possibility–Probability Transformation
      1. Reference
    11. Index