Novel weighting and resampling schemes in Population Monte Carlo

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

Novel weighting and resampling schemes in Population Monte Carlo

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

School of Mathematics

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper we focus on novel population Monte Carlo schemes that present added flexibility and superior performance compared to the standard approach. The new schemes combine different weighting and resampling strategies to achieve more efficient performance at the cost of a reasonable computational complexity increment. Computer simulations compare the different alternatives when applied to the problem of frequency estimation in superimposed sinusoids.

Original language	English
Publication status	Published - 2017
Event	Gretsi 2017 - Juan-les-Pins, France Duration: 5 Sept 2017 → 8 Sept 2017

Conference

Conference	Gretsi 2017
Country/Territory	France
City	Juan-les-Pins
Period	5/09/17 → 8/09/17

Access to Document

elvira2017nouveauxAccepted author manuscript, 453 KB

Cite this

@conference{cc49763d4ff7410693cc0048435ec06e,

title = "Novel weighting and resampling schemes in Population Monte Carlo",

abstract = "In this paper we focus on novel population Monte Carlo schemes that present added flexibility and superior performance compared to the standard approach. The new schemes combine different weighting and resampling strategies to achieve more efficient performance at the cost of a reasonable computational complexity increment. Computer simulations compare the different alternatives when applied to the problem of frequency estimation in superimposed sinusoids.",

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

year = "2017",

language = "English",

note = "Gretsi 2017 ; Conference date: 05-09-2017 Through 08-09-2017",

}

TY - CONF

T1 - Novel weighting and resampling schemes in Population Monte Carlo

AU - Elvira Arregui, Victor

AU - Martino, Luca

AU - Luengo, David

AU - Bugallo, Monica F.

PY - 2017

Y1 - 2017

N2 - In this paper we focus on novel population Monte Carlo schemes that present added flexibility and superior performance compared to the standard approach. The new schemes combine different weighting and resampling strategies to achieve more efficient performance at the cost of a reasonable computational complexity increment. Computer simulations compare the different alternatives when applied to the problem of frequency estimation in superimposed sinusoids.

AB - In this paper we focus on novel population Monte Carlo schemes that present added flexibility and superior performance compared to the standard approach. The new schemes combine different weighting and resampling strategies to achieve more efficient performance at the cost of a reasonable computational complexity increment. Computer simulations compare the different alternatives when applied to the problem of frequency estimation in superimposed sinusoids.

M3 - Paper

T2 - Gretsi 2017

Y2 - 5 September 2017 through 8 September 2017

ER -

Novel weighting and resampling schemes in Population Monte Carlo

Abstract

Conference

Access to Document

Fingerprint

Cite this