Aleatory uncertainty, also called stochastic or variable uncertainty, refers to uncertainty that cannot be reduced by more exhaustive measurements or a better model. Epistemic uncertainty, or subjective uncer-tainty, on the other hand, refers to uncertainty that can be reduced. [3] Despite these apparent distinctions in uncertainty, Aleatory variability and epistemic uncertainty are terms used in seismic hazard analysis that are not commonly used in other fields, but the concepts are well known. Aleatory variability is the natural randomness in a process. For discrete variables, the randomness is parameterized by the probability of each possible value. Aleatory and Epistemic Uncertainty Quantification for Engineering Applications L. P. Swiler*, A. A. Giunta Sandia National Laboratories 1, Albuquerque, NM 87185 USA Abstract Most computer models for engineering applications are developed to help assess a design or regulatory requirement. aleatory and epistemic uncertainties.” Aleatory uncertainty characterizes the inherent randomness in the behavior of the system under study. Alternative terminologies include: variability, stochastic uncertainty, irreducible uncertainty, and Type A uncertainty. Aleatory uncertainty is irreducible except Aleatory vs. Epistemic Uncertainty – Commentary and Observations • Distinction depends on frame of reference (“model of the world”). • Example: Weld crack propagation depends on local material conditions (e.g., Cu content, flaw geometry). – Aleatory model: conditions have a statistical distribution. Directly solving time-dependent reliability-based design optimization (TRBDO) with aleatory and epistemic uncertainties is time-demanding, which limits its engineering application. By treating aleatory and epistemic uncertainties with probability and evidence variables respectively, an advanced decoupling method named sequential optimization and unified time-dependent reliability analysis Finally, epistemic and aleatory uncertainty have distinct markers in natural language. New data (Fox, Ülkümen & Malle, 2011) suggest that epistemic uncertainty tends to be expressed using phrases like “I am 90% sure” or “I’m reasonably confident” whereas aleatory uncertainty tends to be . Epistemic uncertainty derives from the lack of knowledge of a parameter, phenomenon or process, while aleatory uncertainty refers to uncertainty caused by probabilistic variations in a random event . Each of these two different types of uncertainty has its own unique set of characteristics that separate it from the other and can be quantified through different methods. ses are discussed. While many sources of uncertainty may exist, they are generally categorized as either aleatory or epistemic. Uncertainties are characterized as epistemic, if the modeler sees a possibility to reduce them by gathering more data or by refining models. Uncertainties are categorized as aleatory if "Aleatory" and "Epistemic" Uncertainties Terminology/concepts built into multiple documents, e.g., • ASME/ANS PRA Standard • Regulatory Guides 1 200 aleatory uncertainty: the uncertainty inherent in a nondeterministic (s tochastic, random) phenomenon… is reflected by modeling the – 1.200 phenomenon in terms of a probabilistic – 1.174
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There are many forms of uncertainty which afflict measurements and predictions - this video outlines the main ones.
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