LEADER 05876nam 2200757 a 450 001 9910141446203321 005 20200520144314.0 010 $a1-119-94165-2 010 $a1-280-58921-3 010 $a9786613619044 010 $a1-119-96950-6 010 $a1-119-96949-2 035 $a(CKB)2670000000178694 035 $a(EBL)894821 035 $a(SSID)ssj0000638662 035 $a(PQKBManifestationID)11413432 035 $a(PQKBTitleCode)TC0000638662 035 $a(PQKBWorkID)10598956 035 $a(PQKB)10610856 035 $a(Au-PeEL)EBL894821 035 $a(CaPaEBR)ebr10558805 035 $a(CaONFJC)MIL361904 035 $a(OCoLC)793104029 035 $a(CaSebORM)9781119941651 035 $a(MiAaPQ)EBC894821 035 $a(OCoLC)855380990 035 $a(OCoLC)ocn855380990 035 $a(EXLCZ)992670000000178694 100 $a20120104d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModelling under risk and uncertainty $ean introduction to statistical, phenomenological and computational methods /$fEtienne de Rocquigny 205 $a2nd ed. 210 $aChichester, West Sussex, U.K. $cWiley$d2012 215 $a1 online resource (484 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-69514-5 320 $aIncludes bibliographical references and index. 327 $aModelling 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 327 $a1.2.2 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 327 $a1.4.1 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 327 $a2 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 327 $a2.4 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 327 $a2.5.2 Mixed deterministic-probabilistic contexts 330 $a"This volume addresses a concern of very high relevance and growing interest for large industries or environmentalists: risk and uncertainty in complex systems. It gives new insight on the peculiar mathematical challenges generated by recent industrial safety or environmental control analysis, focusing on implementing decision theory choices related to risk and uncertainty analysis through statistical estimation and computation, in the presence of physical modeling and risk analysis. The result will lead statisticians and associated professionals to formulate and solve new challenges at the frontier between statistical modeling, physics, scientific computing, and risk analysis"--$cProvided by publisher. 410 0$aWiley series in probability and statistics. 606 $aIndustrial management$xMathematical models 606 $aUncertainty$xMathematical models 606 $aRisk management$xMathematical models 615 0$aIndustrial management$xMathematical models. 615 0$aUncertainty$xMathematical models. 615 0$aRisk management$xMathematical models. 676 $a338.501/5195 686 $aMAT029000$2bisacsh 700 $aRocquigny$b Etienne de$0522144 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141446203321 996 $aModelling under risk and uncertainty$9835692 997 $aUNINA