Structured variational Bayesian inference for Gaussian state-space models with regime switching

Autor: Francois Desbouvries, Yohan Petetin, Yazid Janati

Publikováno v: IEEE Signal Processing Letters
IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2021, 28, pp.1953-1957. ⟨10.1109/LSP.2021.3113279⟩

International audience; Linear and Gaussian models with regime switching are popular in signal processing. In this letter, we revisit Bayesian inference in such models under the variational Bayesian framework. We propose a structured but implicit var

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bc38f45cea9efb05e71eb830eab784e
https://hal.archives-ouvertes.fr/hal-03348244

A GENERAL PARAMETRIZATION FRAMEWORK FOR PAIRWISE MARKOV MODELS: AN APPLICATION TO UNSUPERVISED IMAGE SEGMENTATION

Autor: Katherine Morales, Yohan Petetin, Hugo Gangloff

Publikováno v: MLSP

Probabilistic graphical models such as Hidden Markov models have found many applications in signal processing. In this paper, we focus on a particular extension of these models, the Pairwise Markov models. We propose a general parametrization of the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df53f4d4297ffba5b649c59ec0bda330
https://hal.archives-ouvertes.fr/hal-03181237

Improving Performances of Log Mining for Anomaly Prediction Through NLP-Based Log Parsing

Autor: Yohan Petetin, Nicolas Aussel, Sophie Chabridon

Publikováno v: MASCOTS
Proceedings MASCOTS 2018: 26th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
MASCOTS 2018: 26th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
MASCOTS 2018: 26th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Sep 2018, Milwaukee, United States. pp.237-243, ⟨10.1109/MASCOTS.2018.00031⟩

International audience; Failure prediction of industrial systems is a promising application domain for data mining approaches and should naturally rely on log messages which are a prime source of data as they are generated by many systems. However, b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a88cd28583f7cf2572568b57c822b5a5
https://doi.org/10.1109/mascots.2018.00031

Semi-independent resampling for particle filtering

Autor: Francois Desbouvries, François Septier, Roland Lamberti, Yohan Petetin

Publikováno v: IEEE Signal Processing Letters
IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2018, 25 (1), pp.130-134. ⟨10.1109/LSP.2017.2775150⟩
IEEE Signal Processing Letters, 2018, 25 (1), pp.130-134. ⟨10.1109/LSP.2017.2775150⟩

Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling (SIR) algorithms are based on Importance Sampling (IS) and on some resampling-based)rejuvenation algorithm which aims at fighting against weight degeneracy. However %whichever

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49cb5a62d1730679a0f06610ed24eda0
https://hal.archives-ouvertes.fr/hal-01670395

An improved SIR-based sequential Monte Carlo algorithm

Autor: Roland Lamberti, Yohan Petetin, Francois Desbouvries, François Septier

Publikováno v: 2016 IEEE Workshop on Statistical Signal Processing (SSP 16)
2016 IEEE Workshop on Statistical Signal Processing (SSP 16), Jun 2016, Palma de Mallorca, Spain
SSP

International audience; Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::630dff84692e8224b2235977f6e9494a
https://imt.hal.science/hal-01359095