Population Monte Carlo schemes with reduced path degeneracy

Victor Elvira; Luca Martino; David Luengo; Monica F. Bugallo

doi:10.1109/CAMSAP.2017.8313090

Population Monte Carlo schemes with reduced path degeneracy

Victor Elvira, Luca Martino, David Luengo, Monica F. Bugallo

School of Mathematics

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

Abstract

Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the resampling step. In this paper we focus on novel population Monte Carlo schemes that present enhanced diversity, compared to the standard approach, while keeping the same implementation structure (sample generation, weighting and resampling). The new schemes combine different weighting and resampling strategies to reduce the path degeneracy and achieve a higher performance at the cost of additional low computational complexity cost. Computer simulations compare the different alternatives in a frequency estimation problem with superimposed sinusoids embedded in Gaussian noise.

Original language	English
Title of host publication	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-5
Number of pages	5
Volume	2017-December
ISBN (Electronic)	978-1-5386-1251-4
ISBN (Print)	978-1-5386-1252-1
DOIs	https://doi.org/10.1109/CAMSAP.2017.8313090
Publication status	Published - 9 Mar 2018
Event	7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao Duration: 10 Dec 2017 → 13 Dec 2017

Conference

Conference	7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
City	Curacao
Period	10/12/17 → 13/12/17

Keywords / Materials (for Non-textual outputs)

Importance sampling
Monte Carlo methods
population Monte Carlo
resampling

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10.1109/CAMSAP.2017.8313090

Cite this

Elvira, V., Martino, L., Luengo, D., & Bugallo, M. F. (2018). Population Monte Carlo schemes with reduced path degeneracy. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 (Vol. 2017-December, pp. 1-5). Article 8313090 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CAMSAP.2017.8313090

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title = "Population Monte Carlo schemes with reduced path degeneracy",

abstract = "Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the resampling step. In this paper we focus on novel population Monte Carlo schemes that present enhanced diversity, compared to the standard approach, while keeping the same implementation structure (sample generation, weighting and resampling). The new schemes combine different weighting and resampling strategies to reduce the path degeneracy and achieve a higher performance at the cost of additional low computational complexity cost. Computer simulations compare the different alternatives in a frequency estimation problem with superimposed sinusoids embedded in Gaussian noise.",

keywords = "Importance sampling, Monte Carlo methods, population Monte Carlo, resampling",

author = "Victor Elvira and Luca Martino and David Luengo and Bugallo, \{Monica F.\}",

year = "2018",

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doi = "10.1109/CAMSAP.2017.8313090",

language = "English",

isbn = "978-1-5386-1252-1",

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booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 ; Conference date: 10-12-2017 Through 13-12-2017",

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Elvira, V, Martino, L, Luengo, D & Bugallo, MF 2018, Population Monte Carlo schemes with reduced path degeneracy. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017. vol. 2017-December, 8313090, Institute of Electrical and Electronics Engineers, pp. 1-5, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017, Curacao, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313090

Population Monte Carlo schemes with reduced path degeneracy. / Elvira, Victor; Martino, Luca; Luengo, David et al.
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017. Vol. 2017-December Institute of Electrical and Electronics Engineers, 2018. p. 1-5 8313090.

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

TY - GEN

T1 - Population Monte Carlo schemes with reduced path degeneracy

AU - Elvira, Victor

AU - Martino, Luca

AU - Luengo, David

AU - Bugallo, Monica F.

PY - 2018/3/9

Y1 - 2018/3/9

N2 - Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the resampling step. In this paper we focus on novel population Monte Carlo schemes that present enhanced diversity, compared to the standard approach, while keeping the same implementation structure (sample generation, weighting and resampling). The new schemes combine different weighting and resampling strategies to reduce the path degeneracy and achieve a higher performance at the cost of additional low computational complexity cost. Computer simulations compare the different alternatives in a frequency estimation problem with superimposed sinusoids embedded in Gaussian noise.

AB - Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the resampling step. In this paper we focus on novel population Monte Carlo schemes that present enhanced diversity, compared to the standard approach, while keeping the same implementation structure (sample generation, weighting and resampling). The new schemes combine different weighting and resampling strategies to reduce the path degeneracy and achieve a higher performance at the cost of additional low computational complexity cost. Computer simulations compare the different alternatives in a frequency estimation problem with superimposed sinusoids embedded in Gaussian noise.

KW - Importance sampling

KW - Monte Carlo methods

KW - population Monte Carlo

KW - resampling

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U2 - 10.1109/CAMSAP.2017.8313090

DO - 10.1109/CAMSAP.2017.8313090

M3 - Conference contribution

AN - SCOPUS:85051142965

SN - 978-1-5386-1252-1

VL - 2017-December

SP - 1

EP - 5

BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017

PB - Institute of Electrical and Electronics Engineers

T2 - 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017

Y2 - 10 December 2017 through 13 December 2017

ER -

Population Monte Carlo schemes with reduced path degeneracy

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