Representation of analysis results involving aleatory and epistemic uncertainty.

Johnson, Jay Dean (ProStat, Mesa, AZ) ; Helton, Jon Craig (Arizona State University, Tempe, AZ) ; Oberkampf, William Louis ; Sallaberry, Cedric J.

Sandia National Laboratories

关键词: Epistemology;    Probability;    43 Particle Accelerators;    Randomness Epistemic Uncertainaty;    Distribution Functions;   

DOI  :  10.2172/940535
RP-ID  :  SAND2008-4379
RP-ID  :  AC04-94AL85000
RP-ID  :  940535

美国|英语

来源: UNT Digital Library

【 摘 要 】

Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for the representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.