Improving population Monte Carlo: Alternative weighting and resampling schemes

Víctor Elvira*, Luca Martino, David Luengo, Mónica F. Bugallo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)77-91
Number of pages15
JournalSignal Processing
Early online date12 Jul 2016
Publication statusPublished - 1 Feb 2017


  • Adaptive importance sampling
  • Population Monte Carlo
  • Proposal distribution
  • Resampling


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