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ASPI : Applications of interacting particle systems to statistics

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ASPI is a joint (INRIA + université de Rennes 1 + université de Rennes 2 + CNRS) project-team, and is part of IRMAR (UMR 6625)

The scientific objectives of ASPI are the design, analysis and implementation of interacting Monte Carlo methods, or particle methods, or sequential Monte Carlo (SMC) methods, dedicated to

ASPI carries methodological research activities, so as to obtain generic results, with techniques borrowed to the many scientific areas that have contributed to the field, i.e.

interacting particle systems, empirical processes, genetic algorithms (GA), hidden Markov models (HMM) and nonlinear filtering, Bayesian statistics, Markov chain Monte Carlo (MCMC) methods, etc.
and implements these techniques and results on some appropriate examples, through collaborations with industrial and academic partners.

Scientific background

Intuitively speaking, interacting Monte Carlo methods are sequential simulation methods, in which particles

The effect of this mutation / selection mechanism is to automatically concentrate particles (i.e. the available computing power) in regions of interest of the state space. In the special case of particle filtering, which has numerous applications under the generic heading of positioning, navigation and tracking, in
target tracking, computer vision, mobile robotics, wireless communications, ubiquitous computing and ambient intelligence, sensor networks, etc.
each particle represents a possible hidden state, and is multiplied or terminated at the next generation on the basis of its consistency with the current observation, as measured by the likelihood function. With these genetic type algorithms, it becomes easy to efficiently combine a prior model of displacement with or without constraints, sensor-based measurements, and a base of reference measurements, for example in the form of a digital map (digital elevation map, attenuation map, etc.). In the most general case, particle methods provide approximations of Feynman-Kac distributions, by means of the weighted empirical probability distribution associated with an interacting particle system, with applications that go far beyond filtering, in
simulation of rare events, black-box optimization, molecular simulation, etc.

Research directions

Industrial and academic relations


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