Mean Field Simulation for Monte Carlo Integration

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Focusing on mean field particle models, this book offers a thorough mathematical analysis, emphasizing refined convergence in nonlinear Markov chain models. It explores diverse applications, including parameter estimation in hidden Markov models, stochastic optimization, and nonlinear filtering. Additionally, it addresses multiple target tracking, calibration, uncertainty propagation in numerical codes, rare event simulation, and concepts from financial mathematics, computational physics, and population biology.