Referenced Items are not available for this document.
We propose a new Monte Carlo method for Bayesian filtering of general nonlinear and non-Gaussian hidden Markov models. This method is an extension of the well known importance sampling method. It is especially well-suited to sequential simulation as it allows one to split or kill trajectories according to a given score function. The model and estimation objectives are described. The new Monte Carlo method is presented. A few results on this method are established and its application to Bayesian filtering is described. Simulation results for several nonlinear and non-Gaussian time series are presented
Published in:
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Date of Conference: 31 Aug-2 Sep 1998
- Page(s):
- 194 - 203
- Meeting Date :
- 31 Aug 1998-02 Sep 1998
- ISSN :
- 1089-3555
- Print ISBN:
- 0-7803-5060-X
- INSPEC Accession Number:
- 6047611
- Conference Location :
- Cambridge
- Digital Object Identifier :
- 10.1109/NNSP.1998.710649
- Product Type:
- Conference Publications
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