Improving population Monte Carlo: Alternative weighting and resampling schemes

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• 作者：Ví ; ctor Elvira^a ; ; Luca Martino^b ; David Luengo^c ; Mó ; nica F. Bugallo^d
• 关键词：Population Monte Carlo ; Adaptive importance sampling ; Proposal distribution ; Resampling
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：131
• 期：Complete
• 页码：77-91
• 全文大小：1810 K

文摘

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.