Abstract
This chapter is not about how to do an uncertainty analysis that quantifies aleatoric uncertainty. Instead it explains, for each of the six analysis steps discussed in Chaps. 2–7, how to proceed with the analysis of the epistemic uncertainties if the computer model also operates with aleatoric uncertainties and the combined effect of the latter is the actual model result. Differences to the case of only epistemic uncertainties are explained step by step. In Step 1, the uncertain parameters of the stochastic models, quantifying the aleatoric uncertainties, need to be identified. In Step 2, their state of knowledge is to be quantitatively expressed by subjective probability distributions. The most prominent difference lies in Step 3 (propagate) where the simulation will need to proceed with two nested sampling loops if the analysis of the aleatoric uncertainties is not already taken care of by the computer model. Distribution functions, quantifying the aleatoric uncertainty, and probabilities (in the classical interpretation) that may be read from those distributions are among the model results. In Step 4, it will be necessary to obtain quantitative expressions of their epistemic uncertainty. Importance measures will need to be computed for the aleatoric uncertainties in the inner simulation loop and for the epistemic uncertainties in the outer loop in Step 5. The epistemic uncertainty of the former importance measures will also be of interest. Step 6 then shows ways of how to present the information obtained on both simulation levels.
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Notes
- 1.
“Given the number of times in which an unknown (nothing known a priori) event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named”.
In definition no. 6 of his essay, it says: “By chance I mean the same as probability”.
- 2.
Caution: Some write the density function of a Beta distribution with parameters α and α + β.
- 3.
Caution: Some write the density function of a Gamma distribution with parameters α and 1/β.
References
Apostolakis, G., & Kaplan, S. (1981). Pitfalls in risk calculations. Reliability Engineering, 2, 135–145.
Bayes, T. (1958). Thomas Bayes‘essay towards solving a problem in the doctrine of chances. Studies in the History of Probability and Statistics, Biometrika, 45(3/4), 296–315.
Box, G. E. P., & Tiao, G. C. (1973). Bayesian inference in statistical analysis. Reading, MA: Addison-Wesley.
Buckley, J. J. (1985). Entropy principles in decision making under risk. Risk Analysis, 5(4), 303–313.
Chang, Y.-H., & Mosleh, A. (1998). Dynamic PRA using ADS with RELAP5 Code as its thermal hydraulic module. In A. Mosleh & R. A. Bari (Eds.), Proceedings of PSAM-4. Berlin: Springer.
Cojazzi, G. (1996). The DYLAM approach for the dynamic reliability analysis of systems. Reliability Engineering and System Safety, 52, 279–296.
Devooght, J., & Smidts, C. (1996). Probabilistic dynamics as a tool for dynamic PSA. Reliability Engineering and System Safety, 52, 185–196.
Hofer, E., Kloos, M., Krzykacz-Hausmann, B., Peschke, J., & Sonnenkalb, M. (2002). Dynamic event trees for probabilistic safety analysis. Eurosafe Conference, Berlin.
Hsueh, K. S., & Mosleh, A. (1996). The development and application of the accident dynamic simulator (ADS) for dynamic probabilistic risk assessment of nuclear power plants. Reliability Engineering and System Safety, 52, 297–314.
Kloos, M., & Peschke, J. (2006). MCDET: A probabilistic dynamics method combining Monte Carlo simulation with the discrete dynamic event tree approach. Nuclear Science and Engineering, 153, 137–156.
Labeau, P. E., Smidts, C., & Swaminathan, S. (2000). Dynamic reliability: Towards an integrated platform for probabilistic risk assessment. Reliability Engineering and System Safety, 68, 219–254.
Siu, N. (1994). Risk assessment for dynamic systems: An overview. Reliability Engineering and System Safety, 43, 43–73.
Winkler, R. L., & Hays, W. L. (1975). Statistics: Probability, inference, and decision (2nd ed.). New York: Holt, Rinehart and Winston.
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Hofer, E. (2018). Uncertainty Analysis When Separation of Uncertainties Is Required. In: The Uncertainty Analysis of Model Results. Springer, Cham. https://doi.org/10.1007/978-3-319-76297-5_9
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