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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 coefficients
evolve according to a Gaussian smoothness prior. Our results show
significant improvements.
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