Multiscale Uncertainty Quantification Based on a Generalized Hidden Markov Model

Abstract

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.