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On the concentration properties of interacting particle processes [electronic resource] / Pierre Del Moral, Peng Hu and Liming Wu.

By: Contributor(s): Material type: TextTextSeries: Foundations and trends in machine learning (Online) ; v. 3, issue 3-4, p. 225-389.Publication details: Hanover, Mass. : Now Publishers, c2012.Description: 1 electronic text (p. [225]-389) : digital fileISBN:
  • 9781601985132 (electronic)
Subject(s): DDC classification:
  • 519.22 23
LOC classification:
  • QA274.A1 D456 2012
Online resources: Available additional physical forms:
  • Also available in print.
Contents:
1. Stochastic particle methods -- 2. Some application domains -- 3. Feynman-Kac semigroup analysis -- 4. Empirical processes -- 5. Interacting empirical processes -- 6. Feynman-Kac particle processes.
Abstract: This paper presents some new concentration inequalities for Feynman-Kac particle processes. We analyze different types of stochastic particle models, including particle profile occupation measures, genealogical tree based evolution models, particle free energies, as well as backward Markov chain particle models. We illustrate these results with a series of topics related to computational physics and biology, stochastic optimization, signal processing and Bayesian statistics, and many other probabilistic machine learning algorithms. Special emphasis is given to the stochastic modeling, and to the quantitative performance analysis of a series of advanced Monte Carlo methods, including particle filters, genetic type island models, Markov bridge models, and interacting particle Markov chain Monte Carlo methodologies.
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Includes bibliographical references (p. 383-389).

1. Stochastic particle methods -- 2. Some application domains -- 3. Feynman-Kac semigroup analysis -- 4. Empirical processes -- 5. Interacting empirical processes -- 6. Feynman-Kac particle processes.

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This paper presents some new concentration inequalities for Feynman-Kac particle processes. We analyze different types of stochastic particle models, including particle profile occupation measures, genealogical tree based evolution models, particle free energies, as well as backward Markov chain particle models. We illustrate these results with a series of topics related to computational physics and biology, stochastic optimization, signal processing and Bayesian statistics, and many other probabilistic machine learning algorithms. Special emphasis is given to the stochastic modeling, and to the quantitative performance analysis of a series of advanced Monte Carlo methods, including particle filters, genetic type island models, Markov bridge models, and interacting particle Markov chain Monte Carlo methodologies.

Pierre Del Moral, Peng Hu and Liming Wu (2012) "On the Concentration Properties of Interacting Particle Processes", Foundations and Trends in Machine Learning: Vol. 3: No 3-4, pp 225-389.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Title from PDF (viewed on 16 April 2012).

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