Adaptive population importance samplers: A general perspective

Luca Martino, Victor Elvira, David Luengo, Francisco Louzada

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

Abstract

Importance sampling (IS) is a well-known Monte Carlo method, widely used to approximate a distribution of interest using a random measure composed of a set of weighted samples generated from another proposal density. Since the performance of the algorithm depends on the mismatch between the target and the proposal densities, a set of proposals is often iteratively adapted in order to reduce the variance of the resulting estimator. In this paper, we review several well-known adaptive population importance samplers, providing a unified common framework and classifying them according to the nature of their estimation and adaptive procedures. Furthermore, we interpret the underlying motivation for the different adaptation schemes, opening the door for novel and more efficient algorithms. Finally, we compare the performance of different algorithms available in the literature through a toy example.

Original languageEnglish
Title of host publication2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016
PublisherIEEE Computer Society
Volume2016-September
ISBN (Electronic)9781509021031
ISBN (Print)978-1-5090-2104-8
DOIs
Publication statusPublished - 15 Sep 2016
Event2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016 - Rio de Rio de Janeiro, Brazil
Duration: 10 Jul 201613 Jul 2016

Conference

Conference2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016
CountryBrazil
CityRio de Rio de Janeiro
Period10/07/1613/07/16

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