Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1154825 | Statistics & Probability Letters | 2012 | 12 Pages |
Abstract
Among the convolution particle filters for discrete-time dynamic systems defined by nonlinear state space models, the Resampled Convolution Filter is one of the most efficient, in terms of estimation of the conditional probability density functions (pdf's) of the state variables and unknown parameters and in terms of implementation. This nonparametric filter is known for its almost sure L1-convergence property. But contrarily to the other convolution filters, its almost sure punctual convergence had not yet been established. This paper is devoted to the proof of this property.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Jean-Pierre Vila,