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B. THE COMPLEMENTARY CUMULATIVE DISTRIBUTION FUNCTION: THE RISK CURVE
Pages 110-115

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From page 110...
... does not represent reality, because it suggests that the outcome of a consequence is known with complete certainty. In practice, there are no absolutes; rather, there is significant uncertainty, starting with the uncertainties of the- many individual inputs that are used to calculate a CCDF for a typical risk assessment.
From page 112...
... 2 Cot 0-4 10-6 A 10-8 a' > (D 10 10 0 12 o o ~ 10 14 · _ ._ D Q o KNAPP: A Potential Solution for the Disposal of Transuranic Waste I I I Illllj I I I 1~1111 1 1 1 {lilll I I I IITII| I I I 111111 1 1 1 111111 1 1 1 llrr,l I I 1 111111 i I I 111' -16 -18 10-5 10-3 10-1 1o1 103 Am' Consequence Value, cE FIGURE B.2 Example distribution of CCDF curves obtained by sampling imprecisely known analysis inputs. Source: Helton et al.
From page 113...
... However, it is the distribution of CCDF curves that provides the overall measure of confidence that can be placed in the results of the risk assessment. The distribution of CCDF curves in Figure B.2, and hence the mean and percentile curves in Figure B.3, are typically obtained by sampling-based techniques and therefore are approximate.
From page 114...
... . I I I I i 11 i i I ~ I I i 11 1 1 1 1 1 1 -- 95th Percentile Mean 50th Percentile -- -- -- - 5th Percentile -_ 10-4 \ \\~\ \' ~ IlIll ~ 1~11 ~ ~ I ~ "1 10 3 10-2 10-1 10° 1o1 102 103 104 Consequence Value, cE FIGURE B.3 Example summary curves derived from an estimated distribution of CCDF curves.
From page 115...
... to-do L ;:- ;; ~ 111411~ 10 10 10 2 10-1 10° 101 102 103 104 Consequence Value, cE 115 FIGURE B.4 Example of mean and percentile curves obtained with two independently generated samples for the results shown In Figure B.3. Source: Helton et al.


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