# Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation

@article{Kelly2019ErgodicityAA,
title={Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation},
author={David Kelly and Andrew M. Stuart},
journal={Chinese Annals of Mathematics, Series B},
year={2019}
}
• Published 26 November 2016
• Environmental Science
• Chinese Annals of Mathematics, Series B
For particle filters and ensemble Kalman filters it is of practical importance to understand how and why data assimilation methods can be effective when used with a fixed small number of particles, since for many large-scale applications it is not practical to deploy algorithms close to the large particle limit asymptotic. In this paper we address this question for particle filters and, in particular, study their accuracy (in the small noise limit) and ergodicity (for noisy signal and…
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