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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.
Title: | Adaptive population importance samplers: a general perspective |
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Author/s: |
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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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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.
Type | Code | Acronym | Leader | Title |
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Government of Spain | TEC2013-41718-R | OTOSiS | Unspecified | Unspecified |
Horizon 2020 | ERC-2014-CoG 647423 | Unspecified | Unspecified | ERC Consolidator Grant SEDAL |
FP7 | 239784 | Unspecified | Unspecified | ERC grant 239784 |
Item ID: | 46532 |
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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 |