Efficient particle-based online smoothing in general hidden Markov models

@article{Westerborn2014EfficientPO,
  title={Efficient particle-based online smoothing in general hidden Markov models},
  author={Johan Westerborn and Jimmy Olsson},
  journal={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2014},
  pages={8003-8007}
}
This paper deals with the problem of estimating expectations of sums of additive functionals under the joint smoothing distribution in general hidden Markov models. Computing such expectations is a key ingredient in any kind of expectation-maximization-based parameter inference in models of this sort. The paper presents a computationally efficient algorithm for online estimation of these expectations in a forward manner. The proposed algorithm has a linear computational complexity in the number… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS
11 Citations
11 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 11 references

Forward smoothing using sequential Monte Carlo

  • Pierre Del Moral, Arnaud Doucet, Sumeetpal S. Singh
  • Tech. Rep., Cambridge University, 2009.
  • 2009
Highly Influential
9 Excerpts

Künsch , “ Monte Carlo approximations for general state - space models

  • Markus Hürzeler, R Hans
  • Robert and George Casella , Monte Carlo…
  • 2004

Monte Carlo approximations for general state-space models

  • Markus Hürzeler, Hans R. Künsch
  • Journal of Computational and Graphical Statistics…
  • 1998
1 Excerpt

Similar Papers

Loading similar papers…