Epistemic uncertainty is intimately linked to the relationship between theory, evidence, and knowledge. The relationships among observed, observable, and unobservable realities express uncertainties that can be characterized as a lack of knowledge about what is known (unknown knowns), what is known to be unknown (known unknowns), and not knowing what is unknown (unknown unknowns). author = "Vu-Linh Nguyen and S{\'e}bastien Destercke and Eyke H{\"u}llermeier", note = "DBLP's bibliographic metadata records provided through http://dblp.org/search In the general literature on uncertainty, a distinction is made between two inherently different sources of uncertainty, which are often referred to as aleatoric and epistemic . Roughly speaking, aleatoric ( aka statistical) uncertainty refers to the notion of randomness, that is, the variability in the outcome of an experiment which is due to inherently random effects. Epistemic uncertainty is an important quantity for the deployment of deep neural networks in safety-critical applications, since it represents how much one can trust predictions on new data. Recently promising works were proposed using noise injection combined with Monte-Carlo sampling at inference time to estimate this quantity (e.g. Monte-Carlo dropout). Epistemic uncertainty refers to limited knowledge we may have about the system (modeled or real). This type of uncertainty is reducible. If we have more information, we can take more measurements, conduct more tests, "buy" more information. Its strongest proponents view it as the only rational framework to deal with uncertainty: “If you want to handle uncertainty, then you must use probability to do it, there is no choice.” “It is very firmly our opinion that the uniquely suitable representation of uncertainty, whether aleatory or epistemic, is probability.” 9 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. A reducible uncertainty is called an epistemic uncertainty. An epistemic uncertainty refers to the deficiencies by a lack of knowledge or information. Reducible uncertainties have two main sources: (1) the statistical uncertainty due to the use of limited samples. Epistemic uncertainty is also referred to as reducible uncertainty, subjective uncertainty, and uncertainty due to lack of knowledge” (, p. 10-2). Aleatory uncertainty refers to variation which is inherent to a given system, typically as a result of the random nature of model inputs. The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related
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Talk by Kathy Puddifoot as part of the 'Challenges to Wellbeing: the experience of loneliness and the threat of epistemic injustice on the clinical encounter... Driving policy function is modeled via Heteroscedastic Mixture Density Network where epistemic uncertainty is measure by the method proposed in [1]. [1] Alex Kendall and Yarin Gal, "What ... Share your videos with friends, family, and the world Part 1 - We already have a unified uncertainty theoryProfessor Anthony O'HaganProbability is the quantification of uncertainty. It handles all kinds of uncer... Talk given by Alex Miller-Tate as part of the 'Challenges to Wellbeing: the experience of loneliness and the threat of epistemic injustice on the clinical en...
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