Majorization as a theory for uncertainty
Volodina, V., Sonenberg, N., Wheatcroft, E.
& Wynn, H.
(2022).
Majorization as a theory for uncertainty.
International Journal for Uncertainty Quantification,
12(5), 23 - 45.
https://doi.org/10.1615/Int.J.UncertaintyQuantification.2022035476
& Wynn, H.
(2022).
Majorization as a theory for uncertainty.
International Journal for Uncertainty Quantification,
12(5), 23 - 45.
https://doi.org/10.1615/Int.J.UncertaintyQuantification.2022035476
Majorization, also called rearrangement inequalities, yields a type of stochastic ordering in which two or more distributions can be compared. In this paper we argue that majorization is a good candidate as a theory for uncertainty. We present operations that can be applied to study uncertainty in a range of settings and demonstrate our approach to assessing uncertainty with examples from well known distributions and from applications of climate projections and energy systems.