Bayesian data assimilation based on a family of outer measures

2016/11/09 by Jérémie Houssineau, Houssineau, Jeremie, Daniel E. Clark +1 · 1 citation
Computer Science · Engineering · #60A10 #62C10 #FOS: Computer and information sciences #Fault Detection and Control Systems #Information Theory (cs.IT) #Reservoir Engineering and Simulation Methods #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1611.02989

openalex publication_date 2016/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• THE GENERALISED PRODUCT MOMENT DISTRIBUTION IN SAMPLES FROM A NORMAL MULTIVARIATE POPULATION

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• Sequential Monte Carlo algorithms for a class of outer measures

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