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A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis
Author(s) -
Iman Ronald L.,
Hora Stephen C.
Publication year - 1990
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1990.tb00523.x
Subject(s) - event (particle physics) , measure (data warehouse) , variance (accounting) , quantile , event tree , fault tree analysis , event tree analysis , probabilistic logic , statistics , econometrics , computer science , uncertainty analysis , mathematics , data mining , reliability engineering , engineering , physics , accounting , quantum mechanics , business
The analysis of probabilistic fault trees often involves the investigation of events that contribute both to the frequency of the top event and to the uncertainty in this frequency. This paper provides a discussion of three measures of the contribution of an event to the total uncertainty in the top event. These measures are known as uncertainty importance measures. Two of these measures are new developments. Each of the measures is shown to have unique advantages and disadvantages. The three measures are based on, respectively, the expected reduction in the variance of the top‐event frequency should the uncertainty in an event be resolved, the same measure based on the log frequency, and a measure based on shifts in the quantiles of the distribution of top‐event frequency.