Non-Bayesian particle filters
Numerical Analysis
2009-05-15 v1 Computation
Abstract
Particle filters for data assimilation in nonlinear problems use "particles" (replicas of the underlying system) to generate a sequence of probability density functions (pdfs) through a Bayesian process. This can be expensive because a significant number of particles has to be used to maintain accuracy. We offer here an alternative, in which the relevant pdfs are sampled directly by an iteration. An example is discussed in detail.
Keywords
Cite
@article{arxiv.0905.2181,
title = {Non-Bayesian particle filters},
author = {Alexandre J. Chorin and Xuemin Tu},
journal= {arXiv preprint arXiv:0905.2181},
year = {2009}
}