LEADER 00814nam0-22002891i-450- 001 990006201030403321 005 19980601 035 $a000620103 035 $aFED01000620103 035 $a(Aleph)000620103FED01 035 $a000620103 100 $a19980601d1965----km-y0itay50------ba 105 $a--------00-yy 200 1 $aComparative Survey of Central Bank Law$fHans Aufricht. 210 $aLondon$cStevens & Sons$d1965 215 $aXII,, 225 p.$d24 cm 225 1 $a<>library of world affairs$v64 676 $a332.11 700 1$aAufricht,$bHans$0111567 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006201030403321 952 $aX O 34 (64)$b75237$fFGBC 959 $aFGBC 996 $aComparative Survey of Central Bank Law$9648500 997 $aUNINA DB $aGIU01 LEADER 00800nam a2200241 i 4500 001 991004362537107536 005 20250117151821.0 008 250117s1982 it ac er 001 0 ita d 040 $aBibl. Dip.le Aggr. Studi Umanistici - Sez. Filosofia$bita$cSocioculturale Scs 041 1 $aita$hger 082 04$a193$223 100 1 $aSchopenhauer, Arthur$0134228 240 10$aGespräche$94309788 245 10$aColloqui /$cArthur Schopenhauer ; prefazione, traduzione e commento di Anacleto Verrecchia 260 $aMilano :$bRizzoli,$c1982 300 $a465 p. :$bill., ritratto ;$c16 cm 490 1 $aIl ramo d'oro 650 4$aFilosofia tedesca$ySec. 19. 700 1 $aVerrecchia, Anacleto 830 3$aIl ramo d'oro 912 $a991004362537107536 996 $aGespräche$94309788 997 $aUNISALENTO LEADER 05457nam 22006974a 450 001 9911019362503321 005 20200520144314.0 010 $a9786611321888 010 $a9781281321886 010 $a1281321885 010 $a9780470725184 010 $a0470725184 010 $a9780470725177 010 $a0470725176 035 $a(CKB)1000000000377270 035 $a(EBL)351165 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(OCoLC)212122308 035 $a(Perlego)2768780 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 $ethe primer /$fAndrea Saltelli ... [et al.] 210 $aChichester, England ;$aHoboken, NJ $cJohn Wiley$dc2008 215 $a1 online resource (306 p.) 300 $aDescription based upon print version of record. 311 08$a9780470059975 311 08$a0470059974 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 $a9911019362503321 996 $aGlobal sensitivity analysis$94418647 997 $aUNINA