Adaptive population importance samplers: a general perspective

Martino, Luca and Elvira Arregui, Víctor and Luengo García, David and Louzada, Francisco (2016). Adaptive population importance samplers: a general perspective. In: "9th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2016)", 10/07/2016 - 13/07/2016, Río de Janeiro (Brasil). ISBN 978-1-5090-2104-8. pp. 1-5. https://doi.org/10.1109/SAM.2016.7569668.

Description

Title: Adaptive population importance samplers: a general perspective
Author/s:
  • Martino, Luca
  • Elvira Arregui, Víctor
  • Luengo García, David
  • Louzada, Francisco
Item Type: Presentation at Congress or Conference (Article)
Event Title: 9th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2016)
Event Dates: 10/07/2016 - 13/07/2016
Event Location: Río de Janeiro (Brasil)
Title of Book: Proceedings of the 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
Date: 2016
ISBN: 978-1-5090-2104-8
Subjects:
Freetext Keywords: Unified common framework, adaptive population importance samplers, importance sampling, Monte Carlo method, proposal density, iterative adaptation, variance reduction
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2013-41718-ROTOSiSUnspecifiedUnspecified
Horizon 2020ERC-2014-CoG 647423UnspecifiedUnspecifiedERC Consolidator Grant SEDAL
FP7239784UnspecifiedUnspecifiedERC grant 239784

More information

Item ID: 46532
DC Identifier: http://oa.upm.es/46532/
OAI Identifier: oai:oa.upm.es:46532
DOI: 10.1109/SAM.2016.7569668
Official URL: http://delamare.cetuc.puc-rio.br/sam2016/index.html
Deposited by: Memoria Investigacion
Deposited on: 29 May 2018 16:25
Last Modified: 29 May 2018 16:25
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