Bayesian data assimilation based on a family of outer measures
Information Theory
2016-11-10 v1 math.IT
Abstract
A flexible representation of uncertainty that remains within the standard framework of probabilistic measure theory is presented along with a study of its properties. This representation relies on a specific type of outer measure that is based on the measure of a supremum, hence combining additive and highly sub-additive components. It is shown that this type of outer measure enables the introduction of intuitive concepts such as pullback and general data assimilation operations.
Keywords
Cite
@article{arxiv.1611.02989,
title = {Bayesian data assimilation based on a family of outer measures},
author = {Jeremie Houssineau and Daniel E. Clark},
journal= {arXiv preprint arXiv:1611.02989},
year = {2016}
}