Mean Field Simulation for Monte Carlo Integration

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The book offers a thorough mathematical exploration of mean field particle models, emphasizing refined convergence analysis for nonlinear Markov chain models. It delves into practical applications such as parameter estimation in hidden Markov models, stochastic optimization, and nonlinear filtering. Additionally, it addresses topics like multiple target tracking, uncertainty propagation in numerical codes, rare event simulation, and connections to financial mathematics, as well as concepts in computational physics and population biology.