Description:
This book presents the first comprehensive and modern mathematical treatment of these mean field particle models, including refined convergence analysis on nonlinear Markov chain models. It also covers applications related to parameter estimation in hidden Markov chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty propagations in numerical codes, rare event simulation, financial mathematics, and free energy and quasi-invariant measures arising in computational physics and population biology.
Review Quotes:
"...I found this to be an enjoyable read. Many illustrative examples reveal intriguing paradoxes in statistical theories, some of them are well-known and complemented with a broad informative discussion and others are less obvious."
--Journal of the American Statistical Association