LEADER 05342nam 22006374a 450 001 9910144716603321 005 20170815114744.0 010 $a1-281-32188-5 010 $a9786611321888 010 $a0-470-72518-4 010 $a0-470-72517-6 035 $a(CKB)1000000000377270 035 $a(EBL)351165 035 $a(OCoLC)748525248 035 $a(SSID)ssj0000163572 035 $a(PQKBManifestationID)11178447 035 $a(PQKBTitleCode)TC0000163572 035 $a(PQKBWorkID)10117597 035 $a(PQKB)10413237 035 $a(MiAaPQ)EBC351165 035 $a(PPN)188612513 035 $a(EXLCZ)991000000000377270 100 $a20071102d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aGlobal sensitivity analysis$b[electronic resource] $ethe primer /$fAndrea Saltelli ... [et al.] 210 $aChichester, England ;$aHoboken, NJ $cJohn Wiley$dc2008 215 $a1 online resource (306 p.) 300 $aDescription based upon print version of record. 311 $a0-470-05997-4 320 $aIncludes bibliographical references (p. [279]-285) and index. 327 $aGlobal Sensitivity Analysis. The Primer; Contents; Preface; 1 Introduction to Sensitivity Analysis; 1.1 Models and Sensitivity Analysis; 1.1.1 Definition; 1.1.2 Models; 1.1.3 Models and Uncertainty; 1.1.4 How to Set Up Uncertainty and Sensitivity Analyses; 1.1.5 Implications for Model Quality; 1.2 Methods and Settings for Sensitivity Analysis - an Introduction; 1.2.1 Local versus Global; 1.2.2 A Test Model; 1.2.3 Scatterplots versus Derivatives; 1.2.4 Sigma-normalized Derivatives; 1.2.5 Monte Carlo and Linear Regression; 1.2.6 Conditional Variances - First Path 327 $a1.2.7 Conditional Variances - Second Path1.2.8 Application to Model (1.3); 1.2.9 A First Setting: 'Factor Prioritization'; 1.2.10 Nonadditive Models; 1.2.11 Higher-order Sensitivity Indices; 1.2.12 Total Effects; 1.2.13 A Second Setting: 'Factor Fixing'; 1.2.14 Rationale for Sensitivity Analysis; 1.2.15 Treating Sets; 1.2.16 Further Methods; 1.2.17 Elementary Effect Test; 1.2.18 Monte Carlo Filtering; 1.3 Nonindependent Input Factors; 1.4 Possible Pitfalls for a Sensitivity Analysis; 1.5 Concluding Remarks; 1.6 Exercises; 1.7 Answers; 1.8 Additional Exercises 327 $a1.9 Solutions to Additional Exercises2 Experimental Designs; 2.1 Introduction; 2.2 Dependency on a Single Parameter; 2.3 Sensitivity Analysis of a Single Parameter; 2.3.1 Random Values; 2.3.2 Stratified Sampling; 2.3.3 Mean and Variance Estimates for Stratified Sampling; 2.4 Sensitivity Analysis of Multiple Parameters; 2.4.1 Linear Models; 2.4.2 One-at-a-time (OAT) Sampling; 2.4.3 Limits on the Number of Influential Parameters; 2.4.4 Fractional Factorial Sampling; 2.4.5 Latin Hypercube Sampling; 2.4.6 Multivariate Stratified Sampling; 2.4.7 Quasi-random Sampling with Low-discrepancy Sequences 327 $a2.5 Group Sampling2.6 Exercises; 2.7 Exercise Solutions; 3 Elementary Effects Method; 3.1 Introduction; 3.2 The Elementary Effects Method; 3.3 The Sampling Strategy and its Optimization; 3.4 The Computation of the Sensitivity Measures; 3.5 Working with Groups; 3.6 The EE Method Step by Step; 3.7 Conclusions; 3.8 Exercises; 3.9 Solutions; 4 Variance-based Methods; 4.1 Different Tests for Different Settings; 4.2 Why Variance?; 4.3 Variance-based Methods. A Brief History; 4.4 Interaction Effects; 4.5 Total Effects; 4.6 How to Compute the Sensitivity Indices; 4.7 FAST and Random Balance Designs 327 $a4.8 Putting the Method to Work: The Infection Dynamics Model4.9 Caveats; 4.10 Exercises; 5 Factor Mapping and Metamodelling; 5.1 Introduction; 5.2 Monte Carlo Filtering (MCF); 5.2.1 Implementation of Monte Carlo Filtering; 5.2.2 Pros and Cons; 5.2.3 Exercises; 5.2.4 Solutions; 5.2.5 Examples; 5.3 Metamodelling and the High-Dimensional Model Representation; 5.3.1 Estimating HDMRs and Metamodels; 5.3.2 A Simple Example; 5.3.3 Another Simple Example; 5.3.4 Exercises; 5.3.5 Solutions to Exercises; 5.4 Conclusions; 6 Sensitivity Analysis: From Theory to Practice 327 $a6.1 Example 1: A Composite Indicator 330 $aComplex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping as 606 $aSensitivity theory (Mathematics) 606 $aGlobal analysis (Mathematics) 606 $aMathematical models 615 0$aSensitivity theory (Mathematics) 615 0$aGlobal analysis (Mathematics) 615 0$aMathematical models. 676 $a003 701 $aSaltelli$b A$g(Andrea),$f1953-$0145511 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144716603321 996 $aGlobal sensitivity analysis$92072070 997 $aUNINA