LEADER 00906nam0-22002891i-450- 001 990005932880403321 005 19980601 035 $a000593288 035 $aFED01000593288 035 $a(Aleph)000593288FED01 035 $a000593288 100 $a19980601f19321939km-y0itay50------ba 105 $a--------00-yy 200 1 $aCode penal Polonais du 11 Juillet 1932 et loi sur les contraventions$fTraduit sous la direction de Stanislas Rappaport, M.Emile. 210 $aParis$cGodde$d[193-?] 215 $a75 p.$d22 cm 676 $a345 700 1$aBerezowski,$bM. Conrad$0403698 702 1$aStanislas Rappaport,$bM.emilE. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005932880403321 952 $aMASS. BU.41 (4)$b41587$fFGBC 959 $aFGBC 996 $aCode penal Polonais du 11 Juillet 1932 et loi sur les contraventions$9586811 997 $aUNINA DB $aGIU01 LEADER 05435nam 22006494a 450 001 9911019911203321 005 20200520144314.0 010 $a9786610539789 010 $a9781280539787 010 $a128053978X 010 $a9780470870945 010 $a047087094X 010 $a9780470870952 010 $a0470870958 035 $a(CKB)111090529061280 035 $a(EBL)189847 035 $a(SSID)ssj0000244166 035 $a(PQKBManifestationID)11188940 035 $a(PQKBTitleCode)TC0000244166 035 $a(PQKBWorkID)10168406 035 $a(PQKB)11042108 035 $a(MiAaPQ)EBC189847 035 $a(OCoLC)85820111 035 $a(PPN)24945498X 035 $a(Perlego)2774745 035 $a(EXLCZ)99111090529061280 100 $a20030929d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSensitivity analysis in practice $ea guide to assessing scientific models /$fAndrea Saltelli ... [et al.] 210 $aHoboken, NJ $cWiley$dc2004 215 $a1 online resource (233 p.) 300 $aDescription based upon print version of record. 311 08$a9780470870938 311 08$a0470870931 320 $aIncludes bibliographical references (p. [211]-216) and index. 327 $aSENSITIVITY ANALYSIS IN PRACTICE; CONTENTS; PREFACE; 1 A WORKED EXAMPLE; 1.1 A simple model; 1.2 Modulus version of the simple model; 1.3 Six-factor version of the simple model; 1.4 The simple model 'by groups'; 1.5 The (less) simple correlated-input model; 1.6 Conclusions; 2 GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT; 2.1 Examples at a glance; 2.2 What is sensitivity analysis?; 2.3 Properties of an ideal sensitivity analysis method; 2.4 Defensible settings for sensitivity analysis; 2.5 Caveats; 3 TEST CASES; 3.1 The jumping man. Applying variance-based methods 327 $a3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variance-based methods3.3 A model of fish population dynamics. Applying the method of Morris; 3.4 The Level E model. Radionuclide migration in the geosphere. Applying variance-based methods and Monte Carlo filtering; 3.5 Two spheres. Applying variance based methods in estimation/calibration problems; 3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems; 3.7 An analytical example. Applying the method of Morris; 4 THE SCREENING EXERCISE 327 $a4.1 Introduction4.2 The method of Morris; 4.3 Implementing the method; 4.4 Putting the method to work: an analytical example; 4.5 Putting the method to work: sensitivity analysis of a fish population model; 4.6 Conclusions; 5 METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT; 5.1 The settings; 5.2 Factors Prioritisation Setting; 5.3 First-order effects and interactions; 5.4 Application of S(i) to Setting 'Factors Prioritisation'; 5.5 More on variance decompositions; 5.6 Factors Fixing (FF) Setting; 5.7 Variance Cutting (VC) Setting; 5.8 Properties of the variance based methods 327 $a5.9 How to compute the sensitivity indices: the case of orthogonal input5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST); 5.10 How to compute the sensitivity indices: the case of non-orthogonal input; 5.11 Putting the method to work: the Level E model; 5.11.1 Case of orthogonal input factors; 5.11.2 Case of correlated input factors; 5.12 Putting the method to work: the bungee jumping model; 5.13 Caveats; 6 SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS 327 $a6.1 Model calibration and Factors Mapping Setting6.2 Monte Carlo filtering and regionalised sensitivity analysis; 6.2.1 Caveats; 6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio; 6.4 Putting MC filtering and RSA to work: the Level E test case; 6.5 Bayesian uncertainty estimation and global sensitivity analysis; 6.5.1 Bayesian uncertainty estimation; 6.5.2 The GLUE case; 6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation; 6.5.4 Implementation of the method; 6.6 Putting Bayesian analysis and global SA to work: two spheres 327 $a6.7 Putting Bayesian analysis and global SA to work: a chemical experiment 330 $aSensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB - a widely distributed freely-available sensitivity anal 606 $aSensitivity theory (Mathematics)$xSimulation methods 615 0$aSensitivity theory (Mathematics)$xSimulation methods. 676 $a003/.5 701 $aSaltelli$b A$g(Andrea),$f1953-$0145511 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019911203321 996 $aSensitivity analysis in practice$94418414 997 $aUNINA