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Keywords
(8)
Complex System
dempster shafer theory
Monte Carlo
Monte Carlo Sampling
Random Set
Reliability Analysis
System Performance
dempster shafer
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Bounds on belief and plausibility of functionally propagated random sets
Bounds on belief and plausibility of functionally propagated random sets,10.1109/NAFIPS.2002.1018095,Cliff Joslyn,Jon C. Helton
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Bounds on belief and plausibility of functionally propagated random sets
(
Citations: 2
)
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Cliff Joslyn
,
Jon C. Helton
We are interested in improving risk and
reliability analysis
of complex systems where our knowledge of
system performance
is provided by large simulation codes, and where moreover input parameters are known only imprecisely. Such imprecision lends itself to interval representations of parameter values, and thence to quantifying our uncertainty through DempsterShafer or Probability Bounds representations on the input space. In this context, the simulation code acts as a large "black box" function f, transforming one input DempsterShafer structure on the line into an output random interval f(A). Our quantification of output uncertainty is then based on this output random interval.. If some properties of f are known, then some information about f(A) can be determined. But when f is a pure black box, we must resort to sampling approaches. We present the basic formalism of a
Monte Carlo
approach to sampling a functionally propagated general random set, as opposed to a random interval. We show that the results of straightforward formal definitions are mathematically coherent, in the sense that bounding and convergence properties are achieved.
Conference:
Conference of the North American Fuzzy Information Processing Society  NAFIPS
, 2002
DOI:
10.1109/NAFIPS.2002.1018095
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Citation Context
(2)
...
1979
, Iman
1992
, Barry
1996
, Fishman
1996
, L'Ecuyer
1998
, Joslyn and Helton
2002
, Helton and Davis
2003
, Joslyn and Kreinovich
2005
)...
Jon C. Helton
,
et al.
Representation of analysis results involving aleatory and epistemic un...
...Plausibility and belief forDs andx(t) are approximated by making the observation thatPly(B) = Plz(f 1(B)) and Bely(B) = Belz(f 1(B)) where y = f(z) is a functional transformation from z to y [
37
]...
Michael Spiegel
.
A Proposal for Computing With Imprecise Probabilities: A Framework for...
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Citations
(2)
Representation of analysis results involving aleatory and epistemic uncertainty
(
Citations: 6
)
Jon C. Helton
,
Jay D. Johnson
,
William L. Oberkampf
,
Cédric J. Sallaberry
Journal:
International Journal of General Systems  INT J GEN SYSTEM
, vol. 39, no. 6, pp. 605646, 2010
A Proposal for Computing With Imprecise Probabilities: A Framework for Multiple Representations of Uncertainty in Simulation Software
Michael Spiegel
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