Efficient Sequential Monte-Carlo Samplers for Bayesian Inference

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 approaches to solve such inference problems. However, in the presence of a high-dimensional and/or multimodal posterior distribution, it is widely documented that standard Monte-Carlo techniques could lead to poor performance. In this paper, the study is focused on a Sequential Monte-Carlo (SMC) sampler framework, 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 aim at proposing some novel strategies that will improve the efficiency and facilitate practical implementation of the SMC sampler specifically for signal processing applications. Firstly, we propose an automatic and adaptive strategy that selects the sequence of distributions within the SMC sampler that minimizes the asymptotic variance of the estimator of the posterior normalization constant. This is critical for performing model selection in modelling applications in Bayesian signal processing. The second original contribution we present improves the global efficiency of the SMC sampler by introducing a novel correction mechanism that allows the use of the particles generated through all the iterations of the algorithm (instead of only particles from the last iteration). This is a significant contribution as it removes the need to discard a large portion of the samples obtained, as is standard in standard SMC methods. This will improve estimation performance in practical settings where computational budget is important to consider.Comment: arXiv admin note: text overlap with arXiv:1303.3123 by other author

Modélisation et estimation des instabilités des émetteurs radars à l'état solide

La stabilité pulse à pulse est une caractéristique primordiale d'un émetteur radar de surface à impulsions car elle limite la détection des cibles dont en particulier celles de faible Surface Equivalente Radar (SER) dans un contexte contraignant (clutter important). L'amélioration des performances des radar passe par une analyse fine des sources possibles de perturbations tels que les variations thermiques des composants de puissance et la fluctuation de l'alimentation. La modélisation statistique est un outil puissant pour exploiter ce type de données radar. Un modèle statistique de signal émis est proposé, il comporte outre l'impulsion dont la forme est variable en fonction de la température et de la fréquence d'émission, un processus multiplicatif qui traduit le comportement basse fréquence de l'alimentation et un bruit additif. Nous proposons ensuite trois coefficients de stabilité de phase de l'alimentation, de l'impulsion en régime transitoire et en régime stationnaire. Nous finirons par analyser les résultats de leurs estimations au regard des conditions thermique de l'expérimentation et de la fréquence à l'émission

Pilot-Aided Sequential Monte Carlo Estimation of Phase Distortions and Transmitted Symbols in Multicarrier Systems

We address the challenging problem of the joint estimation of transmitted symbols and phase distortions in standardized multicarrier systems, including pilot or virtual subcarriers. These subcarriers create time correlation on the useful transmitted OFDM signal that we propose to take into account by an autoregressive model. Because the phase distortions are nonlinear, we set the joint estimation algorithm on the framework of the Sequential Monte Carlo methods. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE); they highlight the efficiency and the robustness of the estimator.Peer Reviewe

Statistical Modeling of SAR Images: A Survey

Statistical modeling is essential to SAR (Synthetic Aperture Radar) image interpretation. It aims to describe SAR images through statistical methods and reveal the characteristics of these images. Moreover, statistical modeling can provide a technical support for a comprehensive understanding of terrain scattering mechanism, which helps to develop algorithms for effective image interpretation and creditable image simulation. Numerous statistical models have been developed to describe SAR image data, and the purpose of this paper is to categorize and evaluate these models. We first summarize the development history and the current researching state of statistical modeling, then different SAR image models developed from the product model are mainly discussed in detail. Relevant issues are also discussed. Several promising directions for future research are concluded at last

Usages et fonctions du concept de structure en économie politique de 1930 à 1980

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"Mythes archaïques et mythes alexandrins dans les Odes d'Horace : valeur politique d'une double réception"

International audienc

"La recusatio dans l'églogue 6 de Virgile : intertextualité et auctorialité"

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Frontières pastorales et frontières génériques dans Les Bucoliques de Virgile : une poétique de la menace

Delignon Bénedicte. Frontières pastorales et frontières génériques dans Les Bucoliques de Virgile : une poétique de la menace. In: Vita Latina, N°174, 2006. pp. 38-50

"La référence philosophique dans la poésie parénétique d'Horace : l'exemple d'Aristippe (Serm. II, 3 et Epist. I, 17)"

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