A Double Proposal Normalized Importance Sampling Estimator

Roland Lamberti; Yohan Petetin; Francois Septier; Francois Desbouvries

doi:10.1109/SSP.2018.8450849

A Double Proposal Normalized Importance Sampling Estimator

Roland Lamberti
, Yohan Petetin
, Francois Septier
, Francois Desbouvries

Université Paris-Saclay
Université de Lille

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

5 Citations (Scopus)

Résumé

Monte Carlo methods are widely used in signal processing for computing integrals of interest. Among Monte Carlo methods, Importance Sampling is a variance reduction technique which consists in sampling from an instrumental distribution and reweighting the samples in order to correct the discrepancy between the target and proposal distributions. When either the target or the proposal distribution is known only up to a constant, the moment of interest can be rewritten as a ratio of two expectations, which can be approximated via self-normalized importance sampling. In this paper we show that it is possible to improve the self-normalized importance sampling estimate by approximating the two expectations in this ratio via two importance distributions. In order to tune them we optimize the variance of the final estimate under a reasonable constraint. Our results are validated via simulations.

langue originale	Anglais
titre	2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	253-257
Nombre de pages	5
ISBN (imprimé)	9781538615706
Les DOIs	https://doi.org/10.1109/SSP.2018.8450849
état	Publié - 29 août 2018
Modification externe	Oui
Evénement	20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Allemagne Durée: 10 juin 2018 → 13 juin 2018

Série de publications

Nom	2018 IEEE Statistical Signal Processing Workshop, SSP 2018

Une conférence

Une conférence	20th IEEE Statistical Signal Processing Workshop, SSP 2018
Pays/Territoire	Allemagne
La ville	Freiburg im Breisgau
période	10/06/18 → 13/06/18

Accès au document

10.1109/SSP.2018.8450849

Contient cette citation

Lamberti, R., Petetin, Y., Septier, F., & Desbouvries, F. (2018). A Double Proposal Normalized Importance Sampling Estimator. Dans 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 (p. 253-257). Article 8450849 (2018 IEEE Statistical Signal Processing Workshop, SSP 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSP.2018.8450849

@inproceedings{81f5158d70e04fdfbd0da00d2044dbdd,

title = "A Double Proposal Normalized Importance Sampling Estimator",

abstract = "Monte Carlo methods are widely used in signal processing for computing integrals of interest. Among Monte Carlo methods, Importance Sampling is a variance reduction technique which consists in sampling from an instrumental distribution and reweighting the samples in order to correct the discrepancy between the target and proposal distributions. When either the target or the proposal distribution is known only up to a constant, the moment of interest can be rewritten as a ratio of two expectations, which can be approximated via self-normalized importance sampling. In this paper we show that it is possible to improve the self-normalized importance sampling estimate by approximating the two expectations in this ratio via two importance distributions. In order to tune them we optimize the variance of the final estimate under a reasonable constraint. Our results are validated via simulations.",

keywords = "(self-normalized) importance sampling, Monte Carlo integration, variance minimization",

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

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 20th IEEE Statistical Signal Processing Workshop, SSP 2018 ; Conference date: 10-06-2018 Through 13-06-2018",

year = "2018",

month = aug,

day = "29",

doi = "10.1109/SSP.2018.8450849",

language = "English",

isbn = "9781538615706",

series = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "253--257",

booktitle = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",

}

Lamberti, R, Petetin, Y, Septier, F & Desbouvries, F 2018, A Double Proposal Normalized Importance Sampling Estimator. Dans 2018 IEEE Statistical Signal Processing Workshop, SSP 2018., 8450849, 2018 IEEE Statistical Signal Processing Workshop, SSP 2018, Institute of Electrical and Electronics Engineers Inc., p. 253-257, 20th IEEE Statistical Signal Processing Workshop, SSP 2018, Freiburg im Breisgau, Allemagne, 10/06/18. https://doi.org/10.1109/SSP.2018.8450849

A Double Proposal Normalized Importance Sampling Estimator. / Lamberti, Roland; Petetin, Yohan; Septier, Francois et al.
2018 IEEE Statistical Signal Processing Workshop, SSP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 253-257 8450849 (2018 IEEE Statistical Signal Processing Workshop, SSP 2018).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - A Double Proposal Normalized Importance Sampling Estimator

AU - Lamberti, Roland

AU - Petetin, Yohan

AU - Septier, Francois

AU - Desbouvries, Francois

PY - 2018/8/29

Y1 - 2018/8/29

N2 - Monte Carlo methods are widely used in signal processing for computing integrals of interest. Among Monte Carlo methods, Importance Sampling is a variance reduction technique which consists in sampling from an instrumental distribution and reweighting the samples in order to correct the discrepancy between the target and proposal distributions. When either the target or the proposal distribution is known only up to a constant, the moment of interest can be rewritten as a ratio of two expectations, which can be approximated via self-normalized importance sampling. In this paper we show that it is possible to improve the self-normalized importance sampling estimate by approximating the two expectations in this ratio via two importance distributions. In order to tune them we optimize the variance of the final estimate under a reasonable constraint. Our results are validated via simulations.

AB - Monte Carlo methods are widely used in signal processing for computing integrals of interest. Among Monte Carlo methods, Importance Sampling is a variance reduction technique which consists in sampling from an instrumental distribution and reweighting the samples in order to correct the discrepancy between the target and proposal distributions. When either the target or the proposal distribution is known only up to a constant, the moment of interest can be rewritten as a ratio of two expectations, which can be approximated via self-normalized importance sampling. In this paper we show that it is possible to improve the self-normalized importance sampling estimate by approximating the two expectations in this ratio via two importance distributions. In order to tune them we optimize the variance of the final estimate under a reasonable constraint. Our results are validated via simulations.

KW - (self-normalized) importance sampling

KW - Monte Carlo integration

KW - variance minimization

U2 - 10.1109/SSP.2018.8450849

DO - 10.1109/SSP.2018.8450849

M3 - Conference contribution

AN - SCOPUS:85053843756

SN - 9781538615706

T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

SP - 253

EP - 257

BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018

Y2 - 10 June 2018 through 13 June 2018

ER -

A Double Proposal Normalized Importance Sampling Estimator

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Contient cette citation