Talker: François Septier (Institut Mines Télécom / Télécom
Lille)
Title: Sequential Monte-Calro Samplers for Bayesian Inference in
Complex Systems
Abstract: In many problems, complex non-Gaussian and/or nonlinear
models are required to accurately describe a physical system of
interest. In such cases, Monte Carlo algorithms are remarkably
flexible and extremely powerful to solve such inference problems.
However, in the presence of high-dimensional and/or multimodal
posterior distribution, standard Monte-Carlo techniques could lead
to poor performance. In this thesis, the study is focused on
Sequential Monte-Carlo Sampler, a more robust and efficient Monte
Carlo algorithm. Although this approach presents many advantages
over traditional Monte-Carlo methods, the potential of this emergent
technique is however largely underexploited in signal processing. In
this work, we therefore focus our study on this technique by aiming
at proposing some novel strategies that will improve the efficiency
and facilitate practical implementation of the SMC sampler. Firstly,
we propose an automatic and adaptive strategy that selects the
sequence of distributions within the SMC sampler that approximately
minimizes the asymptotic variance of the estimator of the posterior
normalizing constant. Secondly, we present an original contribution
in order to improve the global efficiency of the SMC sampler by
introducing some correction mechanisms that allow the use of the
particles generated through all the iterations of the algorithm
(instead of only particles from the last iteration).
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