Florent Chatelain
University of Grenoble
42 Papers
102 Citations
Florent Chatelain is an academic researcher from University of Grenoble. The author has contributed to research in topics: Estimator & Multivariate statistics. The author has an hindex of 8, co-authored 42 publications. Previous affiliations of Florent Chatelain include Centre national de la recherche scientifique.
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Papers
SELFI: an object-based, Bayesian method for faint emission line source detection in MUSE deep field data cubes
Celine Meillier,Florent Chatelain,Olivier Michel,Roland Bacon,Laure Piqueras,Raphael Bacher,Hacheme Ayasso +6 more
TL;DR: SELFI as discussed by the authors is a new Bayesian method optimized for detection of faint galaxies in Multi Unit Spectroscopic Explorer (MUSE) deep fields, which yields a natural sparse representation of the sources in massive data fields, such as MUSE data cubes.
Multi-branch Hidden semi-Markov modeling for RUL prognosis
Thanh Trung Le,Christophe Bérenguer,Florent Chatelain +2 more
- 01 Jan 2015
TL;DR: The results show that the proposed MB-HSMM gives a very promising performance in deterioration mo de detection as well as in the RUL estimation, especially in the case where these deterioration modes exhibit very different dynamics.
Hidden Markov Models for diagnostics and prognostics of systems under multiple deterioration modes
Thanh Trung Le,Florent Chatelain,Christophe Bérenguer +2 more
- 14 Sep 2014
TL;DR: In this article, a multi-branch HMM (MB-HMM) model is proposed to deal with deterioration processes modeling under multiple competing modes, and a numerical study is given to illustrate the proposed approach.
•Posted Content
Bayesian Model for Multiple Change-points Detection in Multivariate Time Series
TL;DR: The Bernoulli Detector relies on the combination of a local robust statistical test based on the computation of ranks and acting on individual time segments, with a global Bayesian framework able to optimize the change-points configurations from multiple local statistics, provided as $p-values.
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Bioinformatics Methods to Select Prognostic Biomarker Genes from Large Scale Datasets: A Review
TL;DR: This review describes studies that have been performed in the last few years on new and promising developments that belong to three main fields of applications, including variable selection concerns various improvements to lasso penalization and the incorporation of biological knowledge through the form of networks or pathways.
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