Adaptive Reconstruction of Imperfectly Observed Monotone Functions, with Applications to Uncertainty Quantification

Adaptive Reconstruction of Imperfectly Observed Monotone Functions, with Applications to Uncertainty Quantification

Motivated by the desire to numerically calculate rigorous upper and lower bounds on deviation probabilities over large classes of probability distributions, we present an adaptive algorithm for the reconstruction of increasing real-valued functions. While this problem is similar to the classical sta...

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Bibliographic Details
Main Authors: Luc Bonnet, Jean-Luc Akian, Éric Savin, T. J. Sullivan
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Algorithms
Subjects:
adaptive approximation
isotonic regression
optimisation under uncertainty
uncertainty quantification
aerodynamic design
Online Access:https://www.mdpi.com/1999-4893/13/8/196

Internet

https://www.mdpi.com/1999-4893/13/8/196

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