Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty

Reliability Engineering and System Safety

Volumn 91, Issue 10-11, 2006, Pages 1414-1434

  Helton, J C ^a   Johnson, J D ^b   Oberkampf, W L ^c   Sallaberry, C J ^c  

^a ARIZONA STATE UNIVERSITY   (United States)

^b ProStat   (United States)

^c SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

Epistemic uncertainty; Evidence theory; Sensitivity analysis; Uncertainty analysis; Variance decomposition

Indexed keywords

COMPUTATION THEORY; DECOMPOSITION; FUNCTIONS; SENSITIVITY ANALYSIS; SPECTRUM ANALYSIS;

COMPLEMENTARY CUMULATIVE PLAUSIBILITY FUNCTION; EPISTEMIC UNCERTAINTY; UNCERTAINTY ANALYSIS; VARIANCE DECOMPOSITION;

SYSTEMS ANALYSIS;

EID: 33745713565     PISSN: 09518320     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ress.2005.11.055     Document Type: Article

References (55)

1. Saltelli A., Chan K., and Scott E.M. Sensitivity analysis (2000), Wiley, New York
2. Parry G.W., and Winter P.W. Characterization and evaluation of uncertainty in probabilistic risk analysis. Nucl Saf 22 1 (1981) 28-42
3. Apostolakis G. The concept of probability in safety assessments of technological systems. Science 250 4986 (1990) 1359-1364
4. Helton J.C. Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. J Statist Comput Simul 57 1-4 (1997) 3-76
5. Turányi T. Sensitivity analysis of complex kinetic systems. Tools and applications. J Math Chem 5 3 (1990) 203-248
6. Cacuci D.G. Sensitivity and uncertainty analysis, vol. 1: theory (2003), CRC Press, Boca Raton, FL
7. Cukier R.I., Levine H.B., and Shuler K.E. Nonlinear sensitivity analysis of multiparameter model systems. J Comput Phys 26 1 (1978) 1-42
8. Sobol' I.M. Sensitivity analysis for nonlinear mathematical models. Math Modeling Comput Exper 1 4 (1993) 407-414
9. Rabitz H., Alis O.F., Shorter J., and Shim K. Efficient input-output model representations. Comput Phys Commun 117 1-2 (1999) 11-20
10. Saltelli A., Tarantola S., and Chan K.P.-S. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41 1 (1999) 39-56
11. McKay M.D., Morrison J.D., and Upton S.C. Evaluating prediction uncertainty in simulation models. Comput Phys Commun 117 1-2 (1999) 44-51
12. Iman RL. Uncertainty and sensitivity analysis for computer modeling applications. In: Cruse TA, editor. Reliability technology-1992, the winter annual meeting of the American Society of Mechanical Engineers, Anaheim, CA, vol. 28. New York: American Society of Mechanical Engineers, Aerospace Division; November 8-13, 1992. p. 153-68.
13. Helton J.C., and Davis F.J. Sampling-based methods. In: Saltelli A., Chan K., and Scott E.M. (Eds). Sensitivity analysis (2000), Wiley, New York 101-153
14. Kleijnen J.P.C., and Helton J.C. Statistical analyses of scatterplots to identify important factors in large-scale simulations, 1: review and comparison of techniques. Reliab Eng Syst Saf 65 2 (1999) 147-185
15. Frey H.C., and Patil S.R. Identification and review of sensitivity analysis methods. Risk Anal 22 3 (2002) 553-578
16. Helton J.C., and Davis F.J. Illustration of sampling-based methods for uncertainty and sensitivity analysis. Risk Anal 22 3 (2002) 591-622
17. Blower S.M., and Dowlatabadi H. Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example. Int Statist Rev 62 2 (1994) 229-243
18. Hamby D.M. A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess 32 2 (1994) 135-154
19. Helton J.C. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliab Eng Syst Saf 42 2-3 (1993) 327-367
20. Klir G.J. Is there more to uncertainty than some probability theorists might have us believe?. Int J Gen Syst 15 (1989) 347-378
21. Klir G.J. On the alleged superiority of probability representation of uncertainty. IEEE Trans Fuzzy Syst 2 (1994) 27-31
22. Howson C., and Urbach P. Scientific reasoning: the Bayesian approach. 2nd ed (1993), Open Court, Chicago
23. Klir G.J., and Wierman M.J. Uncertainty-based information (1999), Physica-Verlag, New York
24. Helton J.C., Johnson J.D., and Oberkampf W.L. An exploration of alternative approaches to the representation of uncertainty in model predictions. Reliab Eng Syst Saf 85 1-3 (2004) 39-71
25. Kangas A.S., and Kangas J. Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis. For Policy Econ 6 (2004) 169-188
26. Klir G.J. Generalized information theory: aims, results, and open problems. Reliab Eng Syst Saf 85 1-3 (2004) 21-38
27. Dempster A.P. Upper and lower probabilities induced by a multivalued mapping. Ann Math Statist 38 (1967) 325-339
28. Shafer G. A mathematical theory of evidence (1976), Princeton University Press, Princeton, NJ
29. Wasserman L. Belief functions and likelihood. Technical report 420, Department of Statistics, Carnegie-Mellon University, Pittsburgh, PA, 1988.
30. Wasserman L.A. Belief functions and statistical inference. Can J Statist 18 3 (1990) 183-196
31. Guan J.W., and Bell D.A. Evidence theory and its applications vols. 1, 2 (1991), North-Holland, New York
32. Halpern J.Y., and Fagin R. Two views of belief: belief as generalized probability and belief as evidence. Artif Intell 54 (1992) 275-317
33. In: Yager R.R., Kacprzyk J., and Fedrizzi M. (Eds). Advances in the Dempster-Shafer theory of evidence (1994), Wiley, New York
34. Feller W. An introduction to probability theory and its applications. 2nd ed vol. 2 (1971), Wiley, New York
35. Helton J.C., Oberkampf W.L., and Johnson J.D. Competing failure risk analysis using evidence theory. Risk Anal 25 4 (2005) 973-995
36. Dempster A.P. Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika 54 2-3 (1967) 515-528
37. Dempster A.P. A generalization of Bayesian inference. J Roy Statist Soc Ser B 30 (1968) 205-247
38. McKay M.D., Beckman R.J., and Conover W.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 2 (1979) 239-245
39. Helton J.C., and Davis F.J. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81 1 (2003) 23-69
40. Iman R.L., and Conover W.J. The use of the rank transform in regression. Technometrics 21 4 (1979) 499-509
41. Hora S.C., and Helton J.C. A distribution-free test for the relationship between model input and output when using Latin hypercube sampling. Reliab Eng Syst Saf 79 3 (2003) 333-339
42. Iman R.L., and Conover W.J. Small sample sensitivity analysis techniques for computer models, with an application to risk assessment. Commun Statist Theory Methods A9 17 (1980) 1749-1842
43. Beckman R.J., and McKay M.D. Monte-Carlo estimation under different distributions using the same simulation. Technometrics 29 2 (1987) 153-160
44. Cooke R.M., and van Noortwijk J.M. Graphical methods. In: Saltelli A., Chan K., and Scott E.M. (Eds). Sensitivity analysis (2000), Wiley, New York 245-264
45. Helton J.C., Johnson J.D., and Oberkampf W.L. Probability of loss of assured safety in temperature dependent systems with multiple weak and strong links. Reliab Eng Syst Saf 91 3 (2006) 320-348
46. Crowder M.J. Classical competing risks (2001), Chapman & Hall/CRC, Boca Raton, FL
47. David H.A., and Moeschberger M.L. The theory of competing risks (1978), Griffin, London
48. Seal H.L. Studies in the history of probability and statistics, XXXV. Multiple decrements or competing risks. Biometrika 64 (1977) 429-439
49. Gail M. Review and critique of some models used in competing risk analysis. Biometrics 31 1 (1975) 209-222
50. Ayyub B.M. Elicitation of expert opinions for uncertainty and risks (2001), CRC Press, Boca Raton, FL
51. Ferson S., Kreinovich V., Ginzburg L., Myers D.S., and Sentz K. Constructing probability boxes and Dempster-Shafer structures. SAND2003-4015 (2003), Sandia National Laboratories, Albuquerque, NM
52. Dubois D., and Prade H. On the combination of evidence in various mathematical frameworks. In: Flamm J., and Luisi T. (Eds). Reliability data collection and analysis (1992), Kluwer Academic Publishers, Boston, MA 213-241
53. Sentz K., and Ferson S. Combination of evidence in Dempster-Shafer theory. SAND2002-0835 (2002), Sandia National Laboratories, Albuquerque, NM
54. Hora S.C., and Iman R.L. Expert opinion in risk analysis: the NUREG-1150 methodology. Nucl Sci Eng 102 4 (1989) 323-331
55. Saltelli A., Tarantola S., Campolongo F., and Ratto M. Sensitivity analysis in practice (2004), Wiley, New York