In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

In the field of uncertainty quantification, uncertainty in the governing equations may assume two forms: aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty can be characterised by known probability distributions whilst epistemic uncertainty arises from a lack of knowledge of probabilistic information. While extensive research efforts have been devoted to the numerical treatmen...

1. Abstract In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. Aleatory uncertainty is classified as objective and irreducible uncertainty with sufficient information on input uncertainty data, whereas epistemic uncertainty is a subjective and reducible uncertainty that stems from lack of knowledge on input uncertainty ...

There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model – uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this ...

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic uncertainty, and develops a rigorous methodology for efficient refinement of epistemic uncertainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The propose...

Future crewed missions to Mars present a maintenance logistics challenge that is unprecedented in human spaceflight. Mission endurance – defined as the time between resupply opportunities – will be significantly longer than previous missions, and therefore logistics planning horizons are longer and the impact of uncertainty is magnified. Maintenance logistics forecasting typically assumes that ...

Chen, Xiaoxiao Ph.D., Purdue University, December 2014. Epistemic Uncertainty Quantification in Scientific Models. Major Professor: Dongbin Xiu. In the field of uncertainty quantification (UQ), epistemic uncertainty often refers to the kind of uncertainty whose complete probabilistic description is not available, largely due to our lack of knowledge about the uncertainty. Quantification of the ...

Engineering design decisions inherently are made under uncertainty. In this paper, we recognize a difference between aleatory uncertainty (due to inherent randomness) and epistemic uncertainty (due to lack of knowledge). Our hypothesis is that, in engineering design decisions, it is valuable to explicitly represent epistemic uncertainty distinctly from aleatory uncertainty. In this paper, we su...

Uncertainty is pervasive in ecology where the difficulties of dealing with sources of uncertainty are exacerbated by variation in the system itself. Attempts at classifying uncertainty in ecology have, for the most part, focused exclusively on epistemic uncertainty. In this paper we classify uncertainty into two main categories: epistemic uncertainty (uncertainty in determinate facts) and lingu...

We argue that people intuitively distinguish epistemic (knowable) uncertainty from aleatory (random) uncertainty and show that the relative salience of these dimensions is reflected in natural language use. We hypothesize that confidence statements (e.g., “I am fairly confident,” “I am 90% sure,” “I am reasonably certain”) communicate a subjective assessment of primarily epistemic uncertainty, ...