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.
General
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