Improving population Monte Carlo : Alternative weighting and resampling schemes

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Elvira , V , Martino , L , Luengo , D & Bugallo , M F 2017 , ' Improving population Monte Carlo : Alternative weighting and resampling schemes ' , Signal Processing , vol. 131 , pp. 77-91 . https://doi.org/10.1016/j.sigpro.2016.07.012

Title: Improving population Monte Carlo : Alternative weighting and resampling schemes
Author: Elvira, Victor; Martino, Luca; Luengo, David; Bugallo, Monica F.
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2017-02
Language: eng
Number of pages: 15
Belongs to series: Signal Processing
ISSN: 0165-1684
URI: http://hdl.handle.net/10138/307547
Abstract: Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations. (C) 2016 Elsevier B.V. All rights reserved.
Subject: Population Monte Carlo
Adaptive importance sampling
Proposal distribution
Resampling
PARTICLE FILTERS
SAMPLING SCHEMES
111 Mathematics
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