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Computational aspects of sequential Monte Carlo filter and smoother

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  • Genshiro Kitagawa

Abstract

Progress in information technologies has enabled to apply computer-intensive methods to statistical analysis. In time series modeling, sequential Monte Carlo method was developed for general nonlinear non-Gaussian state-space models and it enables to consider very complex nonlinear non-Gaussian models for real-world problems. In this paper, we consider several computational problems associated with sequential Monte Carlo filter and smoother, such as the use of a huge number of particles, two-filter formula for smoothing, and parallel computation. The posterior mean smoother and the Gaussian-sum smoother are also considered. Copyright The Institute of Statistical Mathematics, Tokyo 2014

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  • Genshiro Kitagawa, 2014. "Computational aspects of sequential Monte Carlo filter and smoother," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 443-471, June.
    • Handle: RePEc:spr:aistmt:v:66:y:2014:i:3:p:443-471
    DOI: 10.1007/s10463-014-0446-0
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    References listed on IDEAS

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    1. Mark Briers & Arnaud Doucet & Simon Maskell, 2010. "Smoothing algorithms for state–space models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 61-89, February.
    2. Paul Fearnhead & David Wyncoll & Jonathan Tawn, 2010. "A sequential smoothing algorithm with linear computational cost," Biometrika, Biometrika Trust, vol. 97(2), pages 447-464.
    3. Genshiro Kitagawa, 1994. "The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(4), pages 605-623, December.
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    Cited by:

    1. Lau, F. Din-Houn & Gandy, Axel, 2014. "RMCMC: A system for updating Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 99-110.

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