A new strategy for effective learning in population Monte Carlo sampling

Monica F. Bugallo; Victor Elvira; Luca Martino

doi:10.1109/ACSSC.2016.7869636

A new strategy for effective learning in population Monte Carlo sampling

Monica F. Bugallo
, Victor Elvira
, Luca Martino

Office of the Provost

Stony Brook University

IMT Lille Douai & CRIStAL (UMR CNRS 9189)
University of Valencia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target and that this target is gradually getting closer to the true one. Computer simulations show and confirm the improvement of the proposed strategy compared to the traditional PMC method on a simple considered scenario.

Original language	English
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1540-1544
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869636
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers

Conference

Conference	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

Keywords

Importance sampling
Monte Carlo methods
population Monte Carlo

Access to Document

10.1109/ACSSC.2016.7869636

Cite this

Bugallo, M. F., Elvira, V., & Martino, L. (2017). A new strategy for effective learning in population Monte Carlo sampling. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 1540-1544). Article 7869636 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869636

Bugallo, Monica F. ; Elvira, Victor ; Martino, Luca. / A new strategy for effective learning in population Monte Carlo sampling. Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. editor / Michael B. Matthews. IEEE Computer Society, 2017. pp. 1540-1544 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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title = "A new strategy for effective learning in population Monte Carlo sampling",

abstract = "In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target and that this target is gradually getting closer to the true one. Computer simulations show and confirm the improvement of the proposed strategy compared to the traditional PMC method on a simple considered scenario.",

keywords = "Importance sampling, Monte Carlo methods, population Monte Carlo",

author = "Bugallo, \{Monica F.\} and Victor Elvira and Luca Martino",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 ; Conference date: 06-11-2016 Through 09-11-2016",

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Bugallo, MF, Elvira, V & Martino, L 2017, A new strategy for effective learning in population Monte Carlo sampling. in MB Matthews (ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869636, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 1540-1544, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869636

A new strategy for effective learning in population Monte Carlo sampling. / Bugallo, Monica F.; Elvira, Victor; Martino, Luca.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. ed. / Michael B. Matthews. IEEE Computer Society, 2017. p. 1540-1544 7869636 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bugallo, Monica F.

AU - Elvira, Victor

AU - Martino, Luca

PY - 2017/3/1

Y1 - 2017/3/1

N2 - In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target and that this target is gradually getting closer to the true one. Computer simulations show and confirm the improvement of the proposed strategy compared to the traditional PMC method on a simple considered scenario.

AB - In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target and that this target is gradually getting closer to the true one. Computer simulations show and confirm the improvement of the proposed strategy compared to the traditional PMC method on a simple considered scenario.

KW - Importance sampling

KW - Monte Carlo methods

KW - population Monte Carlo

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DO - 10.1109/ACSSC.2016.7869636

M3 - Conference contribution

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1540

EP - 1544

BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016

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Y2 - 6 November 2016 through 9 November 2016

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Bugallo MF, Elvira V, Martino L. A new strategy for effective learning in population Monte Carlo sampling. In Matthews MB, editor, Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society. 2017. p. 1540-1544. 7869636. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2016.7869636

A new strategy for effective learning in population Monte Carlo sampling

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Keywords

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