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Rao-Blackwellised Particle Filtering via Data Augmentation (2001)
| Venue: | In Advances in Neural Information Processing Systems |
| Citations: | 11 - 1 self |
BibTeX
@INPROCEEDINGS{Andrieu01rao-blackwellisedparticle,
author = {Christophe Andrieu and Nando De Freitas and Arnaud Doucet},
title = {Rao-Blackwellised Particle Filtering via Data Augmentation},
booktitle = {In Advances in Neural Information Processing Systems},
year = {2001},
pages = {200--1},
publisher = {MIT Press}
}
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Abstract
In this paper, we extend the Rao-Blackwellised particle filtering method to more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other improvements include the design of an optimal importance proposal distribution and being able to swap the sampling an selection steps to handle outliers. We focus on sequential binary classifiers that consist of linear combinations of basis functions, whose coecients evolve according to a Gaussian smoothness prior. Our results show significant improvements.
Keyphrases
data augmentation rao-blackwellised particle filtering significant improvement gaussian latent variable selection step optimal importance proposal distribution gaussian smoothness linear combination rao-blackwellised particle discrete observation sequential binary classifier complex hybrid model artificial variable basis function
