CARON, François; DEL MORAL, Pierre; DOUCET, Arnaud; PACE, Michele

doi:10.1137/100788987

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CARON, François
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]

DEL MORAL, Pierre
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]

DOUCET, Arnaud
Dept of Statistics & Dept of Computer Science

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SIAM Journal on Control and Optimization. 2011, vol. 49, n° 4, p. 1766-1792

Society for Industrial and Applied Mathematics

Résumé en anglais

We design a mean field and interacting particle interpretation of a class of spatial branching intensity models with spontaneous births arising in multiple-target tracking problems. In contrast to traditional Feynman-Kac type particle models, the transitions of these interacting particle systems depend on the current particle approximation of the total mass process. In the first part, we analyze the stability properties and the long time behavior of these spatial branching intensity distribution flows. We study the asymptotic behavior of total mass processes and we provide a series of weak Lipschitz type functional contraction inequalities. In the second part, we study the convergence of the mean field particle approximations of these models. Under some appropriate stability conditions on the exploration transitions, we derive uniform and non asymptotic estimates as well as a sub-gaussian concentration inequality and a functional central limit theorem. The stability analysis and the uniform estimates presented in the present article seem to be the first results of this type for this class of spatial branching models.< Réduire

Mots clés en anglais

functional central limit theorems

Spatial branching processes

multi-target tracking problems

mean field and interacting particle systems

Feynman-Kac semigroups

uniform estimates w.r.t. time

functional central limit theorems.

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Particle approximations of a class of branching distribution flows arising in multi-target tracking

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