  These lecture notes present 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
the quantitative performance analysis of a series of advanced Monte Carlo
methods, including particle filters, genetic type island models, Markov bridge
models, interacting particle Markov chain Monte Carlo methodologies.
