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Applications of interacting particle methods and approximation of intensity measures arising in multi-object filtering
Michele Pace
INRIA Sud-Ouest


Interacting particle methods are increasingly used to sample from complex high dimensional distributions and have found a wide range of applications in applied probability, Bayesian statistics and information engineering. Understanding rigorously these Monte Carlo simulation tools leads to fascinating mathematics related to Feynman-Kac path integral theory and their interacting particle interpretations. We illustrate these methods by focusing on the approximation of intensity measures of branching distribution flows, with applications to nonlinear multiple target filtering.


Branching distribution flows, PHD filters, Multiple target tracking, Feynman-Kac formulae, Interacting particle methods