Non-Bayesian particle filters

Details Non-Bayesian particle filters Non-Bayesian particle filters

2009-05-13

arxiv.org/abs/0905.2181v1

Mathematics

Numerical Analysis

Scientific paper

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.

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