This book presents some new concentration inequalities for Feynman-Kac particle processes. It analyzes 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. It illustrates 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 ...
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This book presents some new concentration inequalities for Feynman-Kac particle processes. It analyzes 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. It illustrates 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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Add this copy of On the Concentration Properties of Interacting Particle to cart. $35.38, very good condition, Sold by Hay-on-Wye Booksellers rated 3.0 out of 5 stars, ships from Hereford, UNITED KINGDOM, published 2012 by now publishers Inc.
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Very Good. Minor wear at edges/corners. Faint storage marks/scratches to cover and parts of text blocks. Text as new and unread. 176 p. Foundations and Trends (R) in Machine Learning . Intended for professional and scholarly audience.
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