ML Anthology

Fast Particle Smoothing: If I Had a Million Particles

Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang
ICML 2006 pp. 481-488
doi:10.1145/1143844.1143905 /icml/2006/klaas2006icml-fast/

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.

PDF ICML Semantic Scholar

Cite

Text

Klaas et al. "Fast Particle Smoothing: If I Had a Million Particles." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143905

Markdown

[Klaas et al. "Fast Particle Smoothing: If I Had a Million Particles." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/klaas2006icml-fast/) doi:10.1145/1143844.1143905

BibTeX

@inproceedings{klaas2006icml-fast,
  title     = {{Fast Particle Smoothing: If I Had a Million Particles}},
  author    = {Klaas, Mike and Briers, Mark and de Freitas, Nando and Doucet, Arnaud and Maskell, Simon and Lang, Dustin},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {481-488},
  doi       = {10.1145/1143844.1143905},
  url       = {https://mlanthology.org/icml/2006/klaas2006icml-fast/}
}