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Computational structure of a performance assessment involving stochastic and subjective uncertainty
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Challenge problems: uncertainty in system response given uncertain parameters
Challenge problems: uncertainty in system response given uncertain parameters,10.1016/j.ress.2004.03.002,Reliability Engineering & System Safety,Willi
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Challenge problems: uncertainty in system response given uncertain parameters
(
Citations: 95
)
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William L. Oberkampf
,
Jon C. Helton
,
Cliff A. Joslyn
,
Steven F. Wojtkiewicz
,
Scott Ferson
The
risk assessment
community has begun to make a clear distinction between aleatory and
epistemic uncertainty
in theory and in practice. Aleatory uncertainty is also referred to in the literature as variability, irreducible uncertainty, inherent uncertainty, and stochastic uncertainty.
Epistemic uncertainty
is also termed reducible uncertainty, subjective uncertainty, and stateofknowledge uncertainty. Methods to efficiently represent, aggregate, and propagate different types of uncertainty through computational models are clearly of vital importance. The most widely known and developed methods are available within the mathematics of probability theory, whether frequentist or subjectivist. Newer mathematical approaches, which extend or otherwise depart from probability theory, are also available, and are sometimes referred to as generalized
information theory
(GIT). For example, possibility theory,
fuzzy set
theory, and
evidence theory
are three components of GIT. To try to develop a better understanding of the relative advantages and disadvantages of traditional and newer methods and encourage a dialog between the risk assessment, reliability engineering, and GIT communities, a workshop was held. To focus discussion and debate at the workshop, a set of prototype problems, generally referred to as challenge problems, was constructed. The challenge problems concentrate on the representation, aggregation, and propagation of
epistemic uncertainty
and mixtures of epistemic and aleatory uncertainty through two simple model systems. This paper describes the challenge problems and gives numerical values for the different input parameters so that results from different investigators can be directly compared.
Journal:
Reliability Engineering & System Safety  RELIAB ENG SYST SAFETY
, vol. 85, no. 1, pp. 1119, 2004
DOI:
10.1016/j.ress.2004.03.002
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Citation Context
(45)
...It is referred to as variability, random uncertainty, and stochastic uncertainty [
40
]...
Mohamed Sallak
,
et al.
Reliability assessment for multistate systems under uncertainties bas...
...
2004
)...
Samuel Suss
,
et al.
Optimal design processes under uncertainty and reciprocal dependency
...While this assumption may be reasonable in the presence of a single interval, it has been subjected to criticism
14
,
16
, especially in the presence of multiple intervals...
Shankar Sankararaman
,
et al.
Model parameter estimation with imprecise and unpaired data
...Important issues to consider in uncertainty analysis include the propagation of uncertainty in the model, and uncertainty in the input, output, parameters or model structure (Helton 1993; McKay 1995; McKay et al. 1979;
Oberkampf et al. 2004
)...
...The two primary types of uncertainty are referred to as stochastic (aleatory) or subjective (epistemic) uncertainty, which are often simplified to statistical variability and lack of knowledge, respectively (Helton 1993; Helton and Burmaster 1996;
Oberkampf et al. 2004;
Benke et al. 2007)...
Kurt K. Benke
,
et al.
Risk assessment models for invasive species: uncertainty in rankings f...
...Sources of uncertainty may be divided into two types: aleatory and epistemic (
Oberkampf et al. 2004
)...
Kais Zaman
,
et al.
Robustnessbased design optimization under data uncertainty
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Citations
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Reliability assessment for multistate systems under uncertainties based on the DempsterShafer theory
Mohamed Sallak
,
Walter Schön
,
Felipe Aguirre
Journal:
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,
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