Deriving from the Latin noun alea, which refers to a kind of dice game, aleatory was first used in English in the late 17th century to describe things that are dependent on uncertain odds, much like a roll of the dice. The term now describes things that occur by sheer chance or accident, such as the unlucky bounce of a golf shot or the unusual shape of an ink blot. Going a bit further, the term In these circumstances the idea of aleatory uncertainty is naturally associated with the uncertain future of the system (i.e., the occurrence of the events), and epistemic uncertainty reflected in ... The word aleatory derives from the Latin alea, which means the rolling of dice. Thus, an aleatoric uncertainty is one that is presumed to be the intrinsic randomness of a phenomenon. Interestingly, the word is also used in the context of music, film and other arts, where a randomness or improvisation in the performance is implied. Aleatory uncertainty, on the other hand, is a result of statistical variability. It is attributed to intrinsic randomness and is not reducible as more data is collected for a given model. In practice, the distinction between these two types of uncertainty is not always clear, as there are situations where apparent randomness is actually a result of lack of knowledge. 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 . through design modifications. Examples of aleatory uncertainty are component failures or material properties derived from ... Closer to standard, neoclassical economics, decision theorist Peter Fishburn (1994, p. 137) establishes another distinction between ‘aleatory’ and ‘epistemic uncertainty’. ‘Aleatory uncertainty’ corresponds to ‘chance that affects stochastic phenomena not yet consummated’. Aleatory variability is the natural randomness in a process. For discrete variables, the randomness is parameterized by the probability of each possible value. For continuous variables, the randomness is parameterized by the probability density function. Epistemic uncertainty is the scientific uncertainty in the model of the process. It is due to Uncertainty Analysis of Laminar Aeroheating Prediction for Mars Entries, Deepak Bose and Michael Wright (NASA Ames RC), AIAA Paper 2005-4682, 2005. • Uncertainty analysis for peak forebody heating predicted using the DPLR CFD code • 130 input parameters • Monte Carlo sensitivity analysis used to “shortlist” important parameters • Full Monte Carlo uncertainty analysis on shortlisted ... Aleatory Uncertainty Let us start with the overfitting most machine learning practitioners are familiar with: overfitting caused by aleatory uncertainty, simply said overfitting caused by noisy data. Here we have to deal w ith the fact, that the process generating real data, oftentimes, exhibits intrinsic randomness. 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. Some of these methods include simulation, statistical analysis, and measurements
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