Data Assimilation: The Schr\"odinger Perspective
Numerical Analysis
2019-08-15 v5
Abstract
Data assimilation addresses the general problem of how to combine model-based predictions with partial and noisy observations of the process in an optimal manner. This survey focuses on sequential data assimilation techniques using probabilistic particle-based algorithms. In addition to surveying recent developments for discrete- and continuous-time data assimilation, both in terms of mathematical foundations and algorithmic implementations, we also provide a unifying framework from the perspective of coupling of measures, and Schr\"odinger's boundary value problem for stochastic processes in particular.
Cite
@article{arxiv.1807.08351,
title = {Data Assimilation: The Schr\"odinger Perspective},
author = {Sebastian Reich},
journal= {arXiv preprint arXiv:1807.08351},
year = {2019}
}