Paper Info
Title
Bayesian data assimilation based on a family of outer measures.
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.
Year
Venue
Field
2016
arXiv: Information Theory
Mathematical optimization,Measure (mathematics),Infimum and supremum,Outer measure,Data assimilation,Probabilistic logic,Pullback,Mathematics,Bayesian probability
DocType
Volume
Citations 
Journal
abs/1611.02989
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jeremie Houssineau1349.57
Daniel E. Clark236036.76