Sensitivity analysis /
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作 者：edited by Andrea Saltelli, Karen Chan, E. Marian Scott.
分类号：
ISBN：9780471998921
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简介
Summary:
Publisher Summary 1
Scientists who model systems in a wide range of disciplines explain their methods for sensitivity analysis, which ascertains how the variation, numerical or otherwise, in the output of a model, diagnostic or prognostic, can be apportioned, qualitatively or quantitatively, to different sources of variation, and how the given model depends on the information fed into it. They introduce the entire causal assessment chain, from data to predictions, and explain the impact of source uncertainties and the framing assumptions. The CiP data shows the title as Mathematical and Statistical Methods: Sensitivity Analysis. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Publisher Summary 2
Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice, and is an implicit part of any modelling field.
? Offers an accessible introduction to sensitivity analysis
? Covers all the latest research
? Illustrates concepts with numerous examples, applications and case studies
? Includes contributions form the leading researchers active in developing strategies for sensitivity analysis
The principles of sensitivity analysis area carefully described, and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire causal assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A 'hitch-hiker's guide' is included to allow the more experienced reader to readily access specific applications.
Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly form the numerous examples and applications.
目录
Editors' Preface p. xi
Contributors p. xiii
Introduction
What is Sensitivity Analysis p. 3
Introduction p. 3
An example p. 4
Why carry out a sensitivity analysis p. 5
How to perform sensitivity analysis p. 7
Goals of sensitivity analysis p. 8
Properties of various types of sensitivity analysis techniques p. 10
Choice of methods p. 11
About the chapters ahead p. 12
Hitchhiker's Guide to Sensitivity Analysis p. 15
Introduction p. 15
Screening designs p. 17
Differential analysis p. 18
Monte Carlo analysis p. 20
Measures of importance p. 28
Response surface methodology p. 31
FORM and SORM p. 32
Comparing different approaches p. 32
Analytical test models p. 33
When to use what p. 45
Methods
Designs of Experiments p. 51
Content p. 51
Introduction p. 51
Factorial designs p. 53
Fractional factorial designs p. 57
Other designs p. 59
DOE for computer experiments p. 60
More on DOE for computer experiments: the prediction problem p. 61
Conclusion p. 63
Screening Methods p. 65
Introduction p. 65
Definitions p. 66
One-at-a-time (OAT) designs p. 67
Morris's (1991) OAT designs p. 68
Cotter's design p. 73
Andres' iterated fractional factorial design (IFFD) p. 74
Bettonvil's sequential bifurcation p. 77
Conclusions p. 80
Local Methods p. 81
Introduction p. 81
Features of local sensitivities p. 82
Numerical methods for the calculation of local sensitivities p. 83
Derived sensitivities p. 85
Interpretations of sensitivity information p. 87
Initial sensitivities p. 89
Functional sensitivities p. 91
Scaling and self-similarity relations p. 91
Applications of local sensitivities p. 93
Conclusions p. 99
Sampling-Based Methods p. 101
Introduction p. 101
Definition of distributions for subjective uncertainty p. 103
Sampling procedures p. 106
Evaluation of model p. 115
Uncertainity analysis p. 116
Sensitivity analysis p. 121
Summary p. 152
Reliability Algorithms: FORM and SORM Methods p. 155
Introduction p. 155
Brief review of reliability algorithms p. 157
A review of applications p. 161
Summary of recent theoretical advances p. 163
Concluding remarks p. 165
Variance-Based Methods p. 167
Introduction p. 167
Correlation ratios/importance measures p. 168
Method of Sobol' p. 174
The FAST method p. 181
Sampling strategies p. 190
Discussion p. 195
Last remarks on ANOVA decomposition p. 197
Managing the Tyranny of Parameters in Mathematical Modelling of Physical Systems p. 199
Introduction p. 199
High-dimensional model representations p. 203
Examples p. 212
Applications of HDMR p. 216
Conclusions p. 221
Bayesian Sensitivity Analysis p. 225
Sensitivity analysis in Bayesian analysis: an introduction p. 225
Sensitivity to the prior p. 228
Issues in general sensitivity analysis p. 231
Foundational issues p. 233
Stability theory p. 234
Computation of nondominated alternatives p. 235
Extracting additional information p. 236
Maximin solutions p. 239
Discussion p. 239
Appendix p. 240
Graphical Methods p. 245
Introduction p. 245
A simple problem p. 246
Tornado graphs p. 246
Radar graphs p. 247
Generalized reachable sets p. 247
Matrix and overlay scatterplots p. 249
Cobweb plots p. 251
Cobweb plots for local sensitivity: dike-ring reliability p. 256
Radar plots for importance: internal dosimetry p. 258
Scatterplots for steering of directional samples: uplifting and piping p. 261
Conclusions p. 263
Applications
Practical Experience in Applying Sensitivity and Uncertainty Analysis p. 267
Introduction p. 267
The modelling process p. 269
The chapters ahead p. 272
Conclusions p. 274
Scenario and Parametric Sensitivity and Uncertainty Analysis in Nuclear Waste Disposal Risk Assessment: The Case of GESAMAC p. 275
Introduction: the GESAMAC project p. 275
Results for the radionuclide chain p. 278
Discussion p. 291
Sensitivity Analysis for Signal Extraction in Economic Time Series p. 293
Introduction p. 293
General framework p. 295
Model assessment and parameter uncertainty p. 298
Sensitivity analysis p. 304
Conclusions p. 308
A Dataless Precalibration Analysis in Solid State Physics p. 311
Introduction p. 311
Data analysis technique p. 312
Sensitivity analysis p. 313
Discussion p. 314
Conclusions p. 315
Application of First-Order (FORM) and Second-Order (SORM) Reliability Methods: Analysis and Interpretation of Sensitivity Measures Related to Groundwater Pressure Decreases and Resulting Ground Subsidence p. 317
Introduction p. 317
FORM sensitivity information p. 318
Case studies p. 319
Results and discussion p. 322
Conclusions p. 326
One-at-a-Time and Mini-Global Analyses for Characterizing Model Sensitivity in the Nonlinear Ozone Predictions from the US EPA Regional Acid Deposition Model (RADM) p. 329
Introduction p. 329
Methods and experimental design p. 332
Results p. 338
Discussion and conclusions p. 351
Comparing Different Sensitivity Analysis Methods on a Chemical Reactions Model p. 355
Introduction p. 355
Local OAT Approaches: the EOAT and the derivative-based designs p. 355
The KIM model p. 356
Comparing different SA approaches on KIM p. 359
A quantitative SA analysis p. 361
Discussion and conclusions p. 364
An Application of Sensitivity Analysis to Fish Population Dynamics p. 367
Introduction p. 367
General characteristics of pelagic fish assemblages analyzed p. 368
A stage-based modelling approach to analyze fluctuations in pelagic fish populations p. 371
Sensitivity analysis by the Morris method p. 378
Conclusions p. 382
Global Sensitivity Analysis: A Quality Assurance Tool in Environmental Policy Modelling p. 385
Introduction p. 385
Why sensitivity analysis? p. 386
Model structure and uncertainties p. 387
Data availability p. 388
Computing air emissions p. 388
Pressure indicators p. 389
Indicators of total burden p. 391
Pressure-to-decision indices p. 391
Results and discussion p. 393
Conclusions
Assuring the Quality of Models Designed for Predictive Tasks p. 401
Introduction p. 401
The impasse p. 403
A regionalized sensitivity analysis p. 407
Assessing the quality of a model for predictive exposure assessments p. 412
Challenging high-level conceptual insights p. 417
Conclusions p. 419
Fortune and Future of Sensitivity Analysis p. 421
Introduction: sensitivity analysis as an ingredient of modelling p. 421
The fortune p. 422
The future p. 424
Conclusions p. 425
References p. 427
Appendix
Software for Sensitivity Analysis--A Brief Review p. 451
Introduction p. 451
Software for Sensitivity Analysis p. 451
Other Sensitivity Analysis Software p. 459
Generic Algorithms p. 462
Index p. 467
Contributors p. xiii
Introduction
What is Sensitivity Analysis p. 3
Introduction p. 3
An example p. 4
Why carry out a sensitivity analysis p. 5
How to perform sensitivity analysis p. 7
Goals of sensitivity analysis p. 8
Properties of various types of sensitivity analysis techniques p. 10
Choice of methods p. 11
About the chapters ahead p. 12
Hitchhiker's Guide to Sensitivity Analysis p. 15
Introduction p. 15
Screening designs p. 17
Differential analysis p. 18
Monte Carlo analysis p. 20
Measures of importance p. 28
Response surface methodology p. 31
FORM and SORM p. 32
Comparing different approaches p. 32
Analytical test models p. 33
When to use what p. 45
Methods
Designs of Experiments p. 51
Content p. 51
Introduction p. 51
Factorial designs p. 53
Fractional factorial designs p. 57
Other designs p. 59
DOE for computer experiments p. 60
More on DOE for computer experiments: the prediction problem p. 61
Conclusion p. 63
Screening Methods p. 65
Introduction p. 65
Definitions p. 66
One-at-a-time (OAT) designs p. 67
Morris's (1991) OAT designs p. 68
Cotter's design p. 73
Andres' iterated fractional factorial design (IFFD) p. 74
Bettonvil's sequential bifurcation p. 77
Conclusions p. 80
Local Methods p. 81
Introduction p. 81
Features of local sensitivities p. 82
Numerical methods for the calculation of local sensitivities p. 83
Derived sensitivities p. 85
Interpretations of sensitivity information p. 87
Initial sensitivities p. 89
Functional sensitivities p. 91
Scaling and self-similarity relations p. 91
Applications of local sensitivities p. 93
Conclusions p. 99
Sampling-Based Methods p. 101
Introduction p. 101
Definition of distributions for subjective uncertainty p. 103
Sampling procedures p. 106
Evaluation of model p. 115
Uncertainity analysis p. 116
Sensitivity analysis p. 121
Summary p. 152
Reliability Algorithms: FORM and SORM Methods p. 155
Introduction p. 155
Brief review of reliability algorithms p. 157
A review of applications p. 161
Summary of recent theoretical advances p. 163
Concluding remarks p. 165
Variance-Based Methods p. 167
Introduction p. 167
Correlation ratios/importance measures p. 168
Method of Sobol' p. 174
The FAST method p. 181
Sampling strategies p. 190
Discussion p. 195
Last remarks on ANOVA decomposition p. 197
Managing the Tyranny of Parameters in Mathematical Modelling of Physical Systems p. 199
Introduction p. 199
High-dimensional model representations p. 203
Examples p. 212
Applications of HDMR p. 216
Conclusions p. 221
Bayesian Sensitivity Analysis p. 225
Sensitivity analysis in Bayesian analysis: an introduction p. 225
Sensitivity to the prior p. 228
Issues in general sensitivity analysis p. 231
Foundational issues p. 233
Stability theory p. 234
Computation of nondominated alternatives p. 235
Extracting additional information p. 236
Maximin solutions p. 239
Discussion p. 239
Appendix p. 240
Graphical Methods p. 245
Introduction p. 245
A simple problem p. 246
Tornado graphs p. 246
Radar graphs p. 247
Generalized reachable sets p. 247
Matrix and overlay scatterplots p. 249
Cobweb plots p. 251
Cobweb plots for local sensitivity: dike-ring reliability p. 256
Radar plots for importance: internal dosimetry p. 258
Scatterplots for steering of directional samples: uplifting and piping p. 261
Conclusions p. 263
Applications
Practical Experience in Applying Sensitivity and Uncertainty Analysis p. 267
Introduction p. 267
The modelling process p. 269
The chapters ahead p. 272
Conclusions p. 274
Scenario and Parametric Sensitivity and Uncertainty Analysis in Nuclear Waste Disposal Risk Assessment: The Case of GESAMAC p. 275
Introduction: the GESAMAC project p. 275
Results for the radionuclide chain p. 278
Discussion p. 291
Sensitivity Analysis for Signal Extraction in Economic Time Series p. 293
Introduction p. 293
General framework p. 295
Model assessment and parameter uncertainty p. 298
Sensitivity analysis p. 304
Conclusions p. 308
A Dataless Precalibration Analysis in Solid State Physics p. 311
Introduction p. 311
Data analysis technique p. 312
Sensitivity analysis p. 313
Discussion p. 314
Conclusions p. 315
Application of First-Order (FORM) and Second-Order (SORM) Reliability Methods: Analysis and Interpretation of Sensitivity Measures Related to Groundwater Pressure Decreases and Resulting Ground Subsidence p. 317
Introduction p. 317
FORM sensitivity information p. 318
Case studies p. 319
Results and discussion p. 322
Conclusions p. 326
One-at-a-Time and Mini-Global Analyses for Characterizing Model Sensitivity in the Nonlinear Ozone Predictions from the US EPA Regional Acid Deposition Model (RADM) p. 329
Introduction p. 329
Methods and experimental design p. 332
Results p. 338
Discussion and conclusions p. 351
Comparing Different Sensitivity Analysis Methods on a Chemical Reactions Model p. 355
Introduction p. 355
Local OAT Approaches: the EOAT and the derivative-based designs p. 355
The KIM model p. 356
Comparing different SA approaches on KIM p. 359
A quantitative SA analysis p. 361
Discussion and conclusions p. 364
An Application of Sensitivity Analysis to Fish Population Dynamics p. 367
Introduction p. 367
General characteristics of pelagic fish assemblages analyzed p. 368
A stage-based modelling approach to analyze fluctuations in pelagic fish populations p. 371
Sensitivity analysis by the Morris method p. 378
Conclusions p. 382
Global Sensitivity Analysis: A Quality Assurance Tool in Environmental Policy Modelling p. 385
Introduction p. 385
Why sensitivity analysis? p. 386
Model structure and uncertainties p. 387
Data availability p. 388
Computing air emissions p. 388
Pressure indicators p. 389
Indicators of total burden p. 391
Pressure-to-decision indices p. 391
Results and discussion p. 393
Conclusions
Assuring the Quality of Models Designed for Predictive Tasks p. 401
Introduction p. 401
The impasse p. 403
A regionalized sensitivity analysis p. 407
Assessing the quality of a model for predictive exposure assessments p. 412
Challenging high-level conceptual insights p. 417
Conclusions p. 419
Fortune and Future of Sensitivity Analysis p. 421
Introduction: sensitivity analysis as an ingredient of modelling p. 421
The fortune p. 422
The future p. 424
Conclusions p. 425
References p. 427
Appendix
Software for Sensitivity Analysis--A Brief Review p. 451
Introduction p. 451
Software for Sensitivity Analysis p. 451
Other Sensitivity Analysis Software p. 459
Generic Algorithms p. 462
Index p. 467
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