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A sequential smoothing algorithm with linear computational cost.

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A sequential smoothing algorithm with linear computational cost. / Fearnhead, Paul; Wyncoll, David; Tawn, Jonathan.

In: Biometrika, Vol. 97, No. 2, 06.2010, p. 447-464.

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Fearnhead, Paul ; Wyncoll, David ; Tawn, Jonathan. / A sequential smoothing algorithm with linear computational cost. In: Biometrika. 2010 ; Vol. 97, No. 2. pp. 447-464.

Bibtex

@article{df116181cf7544889b73622a354134b5,
title = "A sequential smoothing algorithm with linear computational cost.",
abstract = "In this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N2) computational cost of most smoothers. The new method also overcomes some degeneracy problems in existing algorithms. Through simulation studies we show that substantial gains in efficiency are obtained for practical amounts of computational cost. It is shown both through these simulation studies, and by the analysis of an athletics dataset, that our new method also substantially outperforms the simple filter-smoother, the only other smoother with computational cost that is O(N).",
keywords = "Extreme value theory • Forward-backward algorithm • Particle filtering • Particle smoothing • Two-filter formula",
author = "Paul Fearnhead and David Wyncoll and Jonathan Tawn",
year = "2010",
month = "6",
doi = "10.1093/biomet/asq013",
language = "English",
volume = "97",
pages = "447--464",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",

}

RIS

TY - JOUR

T1 - A sequential smoothing algorithm with linear computational cost.

AU - Fearnhead, Paul

AU - Wyncoll, David

AU - Tawn, Jonathan

PY - 2010/6

Y1 - 2010/6

N2 - In this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N2) computational cost of most smoothers. The new method also overcomes some degeneracy problems in existing algorithms. Through simulation studies we show that substantial gains in efficiency are obtained for practical amounts of computational cost. It is shown both through these simulation studies, and by the analysis of an athletics dataset, that our new method also substantially outperforms the simple filter-smoother, the only other smoother with computational cost that is O(N).

AB - In this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N2) computational cost of most smoothers. The new method also overcomes some degeneracy problems in existing algorithms. Through simulation studies we show that substantial gains in efficiency are obtained for practical amounts of computational cost. It is shown both through these simulation studies, and by the analysis of an athletics dataset, that our new method also substantially outperforms the simple filter-smoother, the only other smoother with computational cost that is O(N).

KW - Extreme value theory • Forward-backward algorithm • Particle filtering • Particle smoothing • Two-filter formula

U2 - 10.1093/biomet/asq013

DO - 10.1093/biomet/asq013

M3 - Journal article

VL - 97

SP - 447

EP - 464

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 2

ER -