scholarly journals Imprecise Probability and Higher Order Vagueness

2017 ◽  
Vol 94 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Susanna Rinard
Keyword(s):  
Higher Order ◽  
Imprecise Probability

scholarly journals Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis

2019 ◽  
Vol 126 ◽  
pp. 227-247 ◽  
Author(s):  
Pengfei Wei ◽  
Jingwen Song ◽  
Sifeng Bi ◽  
Matteo Broggi ◽  
Michael Beer ◽  
...  
Keyword(s):  
Stochastic Analysis ◽  
Rare Events ◽  
Imprecise Probability ◽  
Probability Models

Multiscale Variability and Uncertainty Quantification Based on a Generalized Multiscale Markov Model

2010 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Probability Theory ◽  
Bayesian Inference ◽  
Markov Model ◽  
Uncertainty Quantification ◽  
System Analysis ◽  
Scale Validation ◽  
Uncertainty Propagation ◽  
Bayes Rule ◽  
Imprecise Probability ◽  
Modal Extension

Variability is inherent randomness in systems, whereas uncertainty is due to lack of knowledge. In this paper, a generalized multiscale Markov (GMM) model is proposed to quantify variability and uncertainty simultaneously in multiscale system analysis. The GMM model is based on a new imprecise probability theory that has the form of generalized interval, which is a Kaucher or modal extension of classical set-based intervals to represent uncertainties. The properties of the new definitions of independence and Bayesian inference are studied. Based on a new Bayes’ rule with generalized intervals, three cross-scale validation approaches that incorporate variability and uncertainty propagation are also developed.

Sign in / Sign up

Export Citation Format

Share Document