Bayesian data assimilation based on a family of outer measures
  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. 
Jeremie Houssineau and Daniel E. Clark
cs.IT math.IT