On the concentration properties of interacting particle processes
type: Item
instance of: scholarly article
zbMATH Open document ID: 1280.60057
DOI: 10.1561/2200000026
author: Pierre Del Moral
author: Peng Hu
author: Liming Wu
publication date: 2012-08-08T00:00:00Z
full work available at URL: https://arxiv.org/abs/1107.1948
Mathematics Subject Classification ID: 60K35
Mathematics Subject Classification ID: 65C35
Mathematics Subject Classification ID: 47D08
Mathematics Subject Classification ID: 82C22
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 60E15
zbMATH DE Number: 6064247
zbMATH Keywords: Feynman-Kac particle processes
zbMATH Keywords: concentration inequalities
zbMATH Keywords: advanced Monte Carlo methods
zbMATH Keywords: Bayesian statistics
zbMATH Keywords: computational physics and biology
zbMATH Keywords: stochastic optimization
MaRDI profile type: Publication
OpenAlex ID: W2119909095
arXiv ID: 1107.1948
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title: On the concentration properties of interacting particle processes
published in: Foundations and Trends in Machine Learning
review text: This monograph 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.
reviewed by: Mihai Gradinaru
description: scientific article; zbMATH DE number 6064247