An improved SIR-based Sequential Monte Carlo algorithm

Roland Lamberti; Yohan Petetin; Francois Septier; Francois Desbouvries

doi:10.1109/SSP.2016.7551745

An improved SIR-based Sequential Monte Carlo algorithm

Roland Lamberti
, Yohan Petetin
, Francois Septier
, Francois Desbouvries

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.

Original language	English
Title of host publication	2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467378024
DOIs	https://doi.org/10.1109/SSP.2016.7551745
Publication status	Published - 24 Aug 2016
Event	19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain Duration: 25 Jun 2016 → 29 Jun 2016

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2016-August

Conference

Conference	19th IEEE Statistical Signal Processing Workshop, SSP 2016
Country/Territory	Spain
City	Palma de Mallorca
Period	25/06/16 → 29/06/16

Keywords

Importance sampling
resampling procedures
sequential Monte Carlo

Access to Document

10.1109/SSP.2016.7551745

Cite this

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title = "An improved SIR-based Sequential Monte Carlo algorithm",

abstract = "Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.",

keywords = "Importance sampling, resampling procedures, sequential Monte Carlo",

author = "Roland Lamberti and Yohan Petetin and Francois Septier and Francois Desbouvries",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 19th IEEE Statistical Signal Processing Workshop, SSP 2016 ; Conference date: 25-06-2016 Through 29-06-2016",

year = "2016",

month = aug,

day = "24",

doi = "10.1109/SSP.2016.7551745",

language = "English",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

booktitle = "2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016",

}

Lamberti, R, Petetin, Y, Septier, F & Desbouvries, F 2016, An improved SIR-based Sequential Monte Carlo algorithm. in 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016., 7551745, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2016-August, IEEE Computer Society, 19th IEEE Statistical Signal Processing Workshop, SSP 2016, Palma de Mallorca, Spain, 25/06/16. https://doi.org/10.1109/SSP.2016.7551745

An improved SIR-based Sequential Monte Carlo algorithm. / Lamberti, Roland; Petetin, Yohan; Septier, Francois et al.
2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016. IEEE Computer Society, 2016. 7551745 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An improved SIR-based Sequential Monte Carlo algorithm

AU - Lamberti, Roland

AU - Petetin, Yohan

AU - Septier, Francois

AU - Desbouvries, Francois

PY - 2016/8/24

Y1 - 2016/8/24

N2 - Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.

AB - Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.

KW - Importance sampling

KW - resampling procedures

KW - sequential Monte Carlo

U2 - 10.1109/SSP.2016.7551745

DO - 10.1109/SSP.2016.7551745

M3 - Conference contribution

AN - SCOPUS:84987909399

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

BT - 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016

PB - IEEE Computer Society

T2 - 19th IEEE Statistical Signal Processing Workshop, SSP 2016

Y2 - 25 June 2016 through 29 June 2016

ER -

An improved SIR-based Sequential Monte Carlo algorithm

Abstract

Publication series

Conference

Keywords

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