Methoden vergleichen

	Robuste Ereignisbaumanalyse – Unsicherheitsbewusste Risikopropagierung	Sensitivitätsanalyse mit Ereignisbaumanalyse
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1960s (ETA); robust extensions ~1990s–2000s	Combination formalized in risk and reliability engineering from the 1990s onward
Urheber	H.E. Lambert / Nuclear industry (ETA); robust extensions developed through aerospace and nuclear risk research	Sensitivity analysis: Saltelli et al. (1990s–2000s); Event tree analysis: Watson (1961, WASH-1400 formalization 1975)
Typ	Probabilistic risk assessment with uncertainty propagation	Hybrid quantitative risk analysis method
Wegweisende Quelle	Bedford, T., & Cooke, R. M. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 9780521773201	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975
Aliasnamen	Robust ETA, uncertainty-aware event tree analysis, ETA with uncertainty quantification, robust probabilistic event tree	SA-ETA, ETA sensitivity analysis, event tree sensitivity analysis, probabilistic sensitivity analysis with ETA
Verwandt	6	6
Zusammenfassung	Robust Event Tree Analysis (Robust ETA) extends classical event tree analysis by explicitly accounting for uncertainty in the probability estimates assigned to each branch. Rather than treating branch probabilities as precise point values, the robust approach represents them as intervals, probability distributions, or imprecise probabilities, then propagates that uncertainty through the tree to produce outcome frequency ranges instead of single numbers. This gives decision-makers a clearer picture of the confidence in risk estimates under realistic conditions of incomplete or conflicting information.	Sensitivity analysis with event tree analysis (SA-ETA) is a quantitative risk assessment approach that systematically varies the input probabilities of an event tree model to determine which branch probabilities or initiating event frequencies most strongly influence the calculated probability of undesired outcomes. It extends classical event tree analysis by ranking the uncertainty contributions of individual inputs, thereby guiding risk-reduction efforts toward the parameters that matter most.