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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 |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2012.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a De Rocquigny, Etienne. | |
| 245 | 1 | 0 | |a Modelling Under Risk and Uncertainty : |b an Introduction to Statistical, Phenomenological and Computational Methods. |
| 260 | |a Hoboken : |b John Wiley & Sons, |c 2012. | ||
| 300 | |a 1 online resource (484 pages) | ||
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| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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 kind of decision can be truly supported and how can I handle residual uncertainty? How much refined should the mathematical description be, given the true data limitations? Could the uncertainty be reduced through more data, increased modeling investment or computational budget? Should it be red. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Industrial management |x Mathematical models. | |
| 650 | 0 | |a Uncertainty |x Mathematical models. | |
| 650 | 0 | |a Risk management |x Mathematical models. | |
| 650 | 6 | |a Gestion d'entreprise |x Modèles mathématiques. | |
| 650 | 6 | |a Gestion du risque |x Modèles mathématiques. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Economics |x Microeconomics. |2 bisacsh | |
| 650 | 7 | |a Industrial management |x Mathematical models |2 fast | |
| 650 | 7 | |a Risk management |x Mathematical models |2 fast | |
| 650 | 7 | |a Uncertainty |x Mathematical models |2 fast | |
| 758 | |i has work: |a Modelling under risk and uncertainty (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGcDyCvcdvfGTDQrk7XdpP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a De Rocquigny, Etienne. |t Modelling Under Risk and Uncertainty : An Introduction to Statistical, Phenomenological and Computational Methods. |d Hoboken : John Wiley & Sons, ©2012 |z 9780470695142 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=894821 |z Texto completo |
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