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Biblioteca

MARC Lista (tabellare)
Approximation methods for high dimensional simulation results : parameter sensitivity analysis and propagation of variations for process chains / / Daniela Steffes-lai
Approximation methods for high dimensional simulation results : parameter sensitivity analysis and propagation of variations for process chains / / Daniela Steffes-lai
Autore Steffes-lai Daniela
Pubbl/distr/stampa Berlin : , : Logos Verlag, , [2014]
Descrizione fisica 1 online resource (ix, 219 pages) : illustrations
Disciplina 003.5
Soggetto topico Sensitivity theory (Mathematics) - Simulation methods
Soggetto genere / forma Electronic books.
ISBN 3-8325-9163-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910468001803321
Steffes-lai Daniela  
Berlin : , : Logos Verlag, , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Approximation methods for high dimensional simulation results : parameter sensitivity analysis and propagation of variations for process chains / / Daniela Steffes-lai
Approximation methods for high dimensional simulation results : parameter sensitivity analysis and propagation of variations for process chains / / Daniela Steffes-lai
Autore Steffes-lai Daniela
Pubbl/distr/stampa Berlin : , : Logos Verlag, , [2014]
Descrizione fisica 1 online resource (ix, 219 pages) : illustrations
Disciplina 003.5
Soggetto topico Sensitivity theory (Mathematics) - Simulation methods
ISBN 3-8325-9163-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910795157603321
Steffes-lai Daniela  
Berlin : , : Logos Verlag, , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Approximation methods for high dimensional simulation results : parameter sensitivity analysis and propagation of variations for process chains / / Daniela Steffes-lai
Approximation methods for high dimensional simulation results : parameter sensitivity analysis and propagation of variations for process chains / / Daniela Steffes-lai
Autore Steffes-lai Daniela
Pubbl/distr/stampa Berlin : , : Logos Verlag, , [2014]
Descrizione fisica 1 online resource (ix, 219 pages) : illustrations
Disciplina 003.5
Soggetto topico Sensitivity theory (Mathematics) - Simulation methods
ISBN 3-8325-9163-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910825587503321
Steffes-lai Daniela  
Berlin : , : Logos Verlag, , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sensitivity analysis in practice [[electronic resource] ] : a guide to assessing scientific models / / Andrea Saltelli ... [et al.]
Sensitivity analysis in practice [[electronic resource] ] : a guide to assessing scientific models / / Andrea Saltelli ... [et al.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (233 p.)
Disciplina 003/.5
511.8
Altri autori (Persone) SaltelliA <1953-> (Andrea)
Soggetto topico Sensitivity theory (Mathematics) - Simulation methods
ISBN 1-280-53978-X
9786610539789
0-470-87094-X
0-470-87095-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto SENSITIVITY 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
3.2 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
4.1 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
5.9 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
6.1 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
6.7 Putting Bayesian analysis and global SA to work: a chemical experiment
Record Nr. UNINA-9910143644003321
Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sensitivity analysis in practice [[electronic resource] ] : a guide to assessing scientific models / / Andrea Saltelli ... [et al.]
Sensitivity analysis in practice [[electronic resource] ] : a guide to assessing scientific models / / Andrea Saltelli ... [et al.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (233 p.)
Disciplina 003/.5
511.8
Altri autori (Persone) SaltelliA <1953-> (Andrea)
Soggetto topico Sensitivity theory (Mathematics) - Simulation methods
ISBN 1-280-53978-X
9786610539789
0-470-87094-X
0-470-87095-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto SENSITIVITY 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
3.2 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
4.1 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
5.9 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
6.1 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
6.7 Putting Bayesian analysis and global SA to work: a chemical experiment
Record Nr. UNINA-9910830625803321
Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sensitivity analysis in practice : a guide to assessing scientific models / / Andrea Saltelli ... [et al.]
Sensitivity analysis in practice : a guide to assessing scientific models / / Andrea Saltelli ... [et al.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (233 p.)
Disciplina 003/.5
Altri autori (Persone) SaltelliA <1953-> (Andrea)
Soggetto topico Sensitivity theory (Mathematics) - Simulation methods
ISBN 9786610539789
9781280539787
128053978X
9780470870945
047087094X
9780470870952
0470870958
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto SENSITIVITY 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
3.2 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
4.1 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
5.9 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
6.1 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
6.7 Putting Bayesian analysis and global SA to work: a chemical experiment
Record Nr. UNINA-9911019911203321
Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui