Efficient multiple importance sampling estimators

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

Research output: Contribution to journalArticlepeer-review

Abstract

Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.

Original languageEnglish
Article number7105865
Pages (from-to)1757-1761
Number of pages5
JournalIEEE Signal Processing Letters
Volume22
Issue number10
Early online date12 May 2015
DOIs
Publication statusPublished - 1 Oct 2015

Keywords

  • Adaptive importance sampling
  • Deterministic mixture
  • Monte Carlo methods
  • Multiple importance sampling

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