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. 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. • These two types of uncertainty may be called, respectively, • the aleatory uncertainty and • the epistemic uncertainty. • The two types of uncertainty may be combined and analyzed as a total uncertainty, or treated separately. In either case, the principles of probability and statistics apply equally. Aleatory uncertainty is also called stochastic, variability, irreducible and type A uncertainty. Aleatory uncertainties are usually modeled with probability distributions, but epistemic uncertainty may or may not be modeled probabilistically. Regulatory agencies, design teams, and weapon certification assessments are increasingly being asked to ... epistemic uncertainty (the likelihood that I answered this item correctly) and aleatory uncertainty (the proportion of times I answered correctly) rely on distinct information, weights, and/or processes. 2. Focus of prediction Assessment of purely epistemic uncertainty generally entails evaluation of events that are (or will be) either true or false. Aleatory and epistemic uncertainty characterize subsurface flow. Aleatory uncertainty is the inherent variation in the physical system; it is stochastic, irreducible uncertainty. Epistemic uncertainty we associate with a lack of knowledge of the quantities or processes identified with the system; it can be subjective, is reducible and may be identified with model uncertainty. 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. We defined the source of aleatory uncertainty as inter-annual variability of wave power and the source of epistemic uncertainty as difference between a model and an observation. Spectral analysis of inter-annual variability and correlation coefficient analysis between inter-annual variability and climate index such as Arctic Oscillation are conducted to quantify aleatory uncertainty. it seems that epistemic and aleatory uncertainty di er in how they focus judgment. Epistemic uncertainty requires gauging one’s con dence for events with a binary truth value (they are, or will be, either true or false). Aleatory uncertainty, on the other hand, entails evaluating propensities along a continuous unit interval.
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Dr. Christophe Simon's talk at the 5th Annual BayesiaLab Conference in Paris. Prof. Scott Ferson (University of Liverpool, United Kingdom) held his presentation “Cognitive biases arise from conflating epistemic and aleatory uncertainty... There are many forms of uncertainty which afflict measurements and predictions - this video outlines the main ones.
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