Aleatory or epistemic? Does it matter?

By A. Der Kiureghian, Ove D. Ditlevsen. 2009

2,651 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

A General Framework for Uncertainty Estimation in Deep Learning (2019, methodology): “To improve the quality of uncertainty estimates, this work proposes to apply ideas borrowed by Bayesian inference [5] to deep learning models.”
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference (2019, background): “[9] [30].”
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “, through stochastic behavior) while epistemic uncertainty (ambiguity) is the uncertainty due to limited data or knowledge.(15,33,34) For example, an uncertainty-aware image classifier should exhibit high aleatoric uncertainty for images that are similar to those it was trained on, but that do not c”
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? (2017, background): “In Bayesian modeling, there are two main types of uncertainty one can model [7].”
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2020, background): “focus on those types that we can quantify in ML models: aleatoric uncertainty (also known as indirect uncertainty) and epistemic uncertainty (also known as direct uncertainty) [39, 40, 56].”
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference (2018, background): “The former can be improved by gathering more data, whereas the latter requests to refine the model [32].”
Impact of copulas for modeling bivariate distributions on system reliability (2013, background): “The reliability results associated with the Gaussian copula are highlighted because the Gaussian copula is commonly used to describe the dependence structure among variables in practice when the available data are not enough to select one copula among a set of candidate copulas [6,15].”
Detecting hallucinations in large language models using semantic entropy (2024, neutral): “Researchers some-times separate aleatoric uncertainty (uncertainty in the underlying data distribution) from epistemic uncertainty (caused by having only limited information) 46 .”
Bayesian Convolutional Neural Networks with Variational Inference (2018, background): “The former can be improved by gathering more data, whereas the latter requests to refine the model [4].”
A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, positive): “According to this classification one distinguishes between epistemic and aleatory uncertainty [22, 23].”
Bayesian Convolutional Neural Networks (2018, methodology): “certainty), or the model itself is the cause for poor performances (i.e. high epistemic uncertainty). The former can be improved by gathering more data, whereas the latter requests to reﬁne the model [4]. 6. Experiments For all conducted experiments, we implement the foregoing description of Bayesia”
Analysis of structural reliability under parameter uncertainties (2008, background): “As noted in [6,13], this is due to the effect of dependence among estimated states of system components, which arises from parameter uncertainties that are common to all components.”
Probabilistic numerics and uncertainty in computations (2015, neutral): “[15,16]), which identifies degrees of freedom in ill-posed problems, and where epistemic uncertainty arises from the set-up of the computation, rather than the computation itself.”
Energy systems modeling for twenty-first century energy challenges (2014, background): “One can differentiate between two fundamental types of uncertainty relevant to modeling: epistemic and aleatory uncertainty (Kiureghian and Ditlevsen, 2009).”