A Comparison of Clipping Strategies for Importance Sampling

L. Martino, V. Elvira, J. Miguez, A. Artes-Rodriguez, P. M. Djuric

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Importance Sampling (IS) methods approximate a targeted distribution with a set of weighted samples, drawn from a proposal distribution. Unfortunately, a mismatch between the proposal and the targeted distribution may endanger the performance of the estimators. In this paper, we focus on the so-called nonlinear IS (NIS) framework, where a nonlinear function is applied to the standard importance weights (IWs). The aim of this transformation is to mitigate the well-known problem of the degeneracy of the IWs by controlling the weight variability. We consider the clipping transformation and test its robustness with respect to the choice of the clipping value. We also propose a novel NIS methodology, where not only a subset of weights is modified a posteriori, but also the corresponding samples are moved. We compare these NIS schemes with standard IS and Monte Carlo methods by means of illustrative numerical examples.

Original languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages125-129
Number of pages5
ISBN (Electronic)978-1-5386-1571-3
ISBN (Print)9781538615706
DOIs
Publication statusPublished - 29 Aug 2018
Event20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018

Conference

Conference20th IEEE Statistical Signal Processing Workshop, SSP 2018
CountryGermany
CityFreiburg im Breisgau
Period10/06/1813/06/18

Keywords

  • Bayesian Inference
  • Importance Sampling
  • Monte Carlo methods
  • Parameter Estimation
  • Variance Reduction methods

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