Fast particle smoothing: if I had a million particles

Authors:
Mike Klaas;Mark Briers;Nando de Freitas;Arnaud Doucet;Simon Maskell;Dustin Lang
Affiliations:
University of British Columbia, Canada;Cambridge University, UK;University of British Columbia, Canada;University of British Columbia, Canada;Advanced Signal and Information Processing Group, QinetiQ, UK;University of Toronto, Canada
Venue:
ICML '06 Proceedings of the 23rd international conference on Machine learning
Year:
2006

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Abstract

We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2) algorithm, where N is the number of particles. We overcome this problem by integrating dual tree recursions and fast multipole techniques with forward-backward smoothers, a new generalized two-filter smoother and a maximum a posteriori (MAP) smoother. Our experiments show that these improvements can substantially increase the practicality of particle smoothing.