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Modelling Under Risk and Uncertainty : an Introduction to Statistical, Phenomenological and Computational Methods.
Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated:How uncertain is my model? Is it truly valuable to support decision-making? What k...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2012.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods; Contents; Preface; Acknowledgements; Introduction and reading guide; Notation; Acronyms and abbreviations; 1 Applications and practices of modelling, risk and uncertainty; 1.1 Protection against natural risk; 1.1.1 The popular 'initiator/frequency approach'; 1.1.2 Recent developments towards an 'extended frequency approach'; 1.2 Engineering design, safety and structural reliability analysis (SRA); 1.2.1 The domain of structural reliability.
- 1.2.2 Deterministic safety margins and partial safety factors1.2.3 Probabilistic structural reliability analysis; 1.2.4 Links and differences with natural risk studies; 1.3 Industrial safety, system reliability and probabilistic risk assessment (PRA); 1.3.1 The context of systems analysis; 1.3.2 Links and differences with structural reliability analysis; 1.3.3 The case of elaborate PRA (multi-state, dynamic); 1.3.4 Integrated probabilistic risk assessment (IPRA); 1.4 Modelling under uncertainty in metrology, environmental/sanitary assessment and numerical analysis.
- 1.4.1 Uncertainty and sensitivity analysis (UASA)1.4.2 Specificities in metrology/industrial quality control; 1.4.3 Specificities in environmental/health impact assessment; 1.4.4 Numerical code qualification (NCQ), calibration and data assimilation; 1.5 Forecast and time-based modelling in weather, operations research, economics or finance; 1.6 Conclusion: The scope for generic modelling under risk and uncertainty; 1.6.1 Similar and dissimilar features in modelling, risk and uncertainty studies; 1.6.2 Limitations and challenges motivating a unified framework; References.
- 2 A generic modelling framework2.1 The system under uncertainty; 2.2 Decisional quantities and goals of modelling under risk and uncertainty; 2.2.1 The key concept of risk measure or quantity of interest; 2.2.2 Salient goals of risk/uncertainty studies and decision-making; 2.3 Modelling under uncertainty: Building separate system and uncertainty models; 2.3.1 The need to go beyond direct statistics; 2.3.2 Basic system models; 2.3.3 Building a direct uncertainty model on variable inputs; 2.3.4 Developing the underlying epistemic/aleatory structure; 2.3.5 Summary.
- 2.4 Modelling under uncertainty
- the general case2.4.1 Phenomenological models under uncertainty and residual model error; 2.4.2 The model building process; 2.4.3 Combining system and uncertainty models into an integrated statistical estimation problem; 2.4.4 The combination of system and uncertainty models: A key information choice; 2.4.5 The predictive model combining system and uncertainty components; 2.5 Combining probabilistic and deterministic settings; 2.5.1 Preliminary comments about the interpretations of probabilistic uncertainty models.