Multiscale Uncertainty Quantification Based on a Generalized Hidden Markov Model
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Variability is the inherent randomness in systems, whereas incertitude is due to lack of knowledge. In this paper, a generalized hidden Markov model (GHMM) is proposed to quantify aleatory and epistemic uncertainties simultaneously in multiscale system analysis. The GHMM is based on a new imprecise probability theory that has the form of generalized interval. The new interval probability resembles the precise probability and has a similar calculus structure. The proposed GHMM allows us to quantify cross-scale dependency and information loss between scales. Based on a generalized interval Bayes’ rule, three cross-scale information assimilation approaches that incorporate uncertainty propagation are also developed.
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2013 ◽
pp. 149-154
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2012 ◽
Vol 2
(6)
◽
pp. 208-211
2012 ◽
Vol 132
(10)
◽
pp. 1589-1594
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