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The time-varying transition probability uncertainty is described as a polytope set.
General relations among the probability, uncertainty of the system and uncertainties of the subsystems are derived by using information theory.
The aim of this paper is to present a robust topology optimization methodology for structures with imprecise probability uncertainty.
This results allows the robust controller design to deal with convex bounded parameter uncertainty, probability uncertainty and cluster availability of the Markov mode.
However, biomedical data are full of probability, uncertainty, incompleteness, vagueness, noise, etc., which makes the application of automated approaches difficult, yet often impossible.
May it become the ultimate source of new results in Probability, Uncertainty and Quantitative Risk and succeed to become a tool of knowledge and exchange for our community.
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In this paper, the imprecise probability uncertainties are treated with an interval random model, in which the probability variables are used to model the uncertain parameters and some distribution parameters of probability variables are expressed as interval variables instead of a precise value.
We have probabilities, uncertainty and distributions of outcomes.
Their pluralism also tends to make them more comfortable with probability and uncertainty and more inclined toward Bayeisan methods.
Professor Siegel concedes the point but says that if the probability of uncertainty increases over time, it would do so for all asset classes.
Further, the systems and subsystems are subjected to probability and uncertainty analysis.
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