Optimal SIR algorithm vs. fully adapted auxiliary particle filter: A matter of conditional independence. - François Desbouvries; Yohan Petetin; Emmanuel Monfrini (ICASSP, 2011)

Direct, prediction- and smoothing-based Kalman and particle filter algorithms. - François Desbouvries; Yohan Petetin; Boujemaa Ait-El-Fquih (Signal Process., 2011)

Multi-object filtering for pairwise Markov chains. - Yohan Petetin; François Desbouvries (ISSPA, 2012)

Marginalized PHD Filters for multi-target filtering. - Yohan Petetin; François Desbouvries (ISSPA, 2012)

A mixed GM/SMC implementation of the probability hypothesis density filter. - Yohan Petetin; François Desbouvries (ISSPA, 2012)

A semi-exact sequential Monte Carlo filtering algorithm in Hidden Markov Chains. - Yohan Petetin; François Desbouvries (ISSPA, 2012)

Further Rao-Blackwellizing an already Rao-Blackwellized algorithm for Jump Markov State Space Systems. - Yohan Petetin; François Desbouvries (ISSPA, 2012)

Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis. - Yohan Petetin; François Desbouvries (Stat. Comput., 2013)

Bayesian Multi-Object Filtering for Pairwise Markov Chains. - Yohan Petetin; François Desbouvries (IEEE Trans. Signal Process., 2013)

Exact Bayesian estimation in constrained Triplet Markov Chains. - Yohan Petetin; François Desbouvries (MLSP, 2014)

Marginalized particle PHD filters for multiple object Bayesian filtering. - Yohan Petetin; Mark R. Morelande; François Desbouvries (IEEE Trans. Aerosp. Electron. Syst., 2014)

Filtrage statistique optimal rapide dans des systèmes linéaires à sauts non stationnaires. - Noufel Abbassi; Stéphane Derrode; François Desbouvries; Yohan Petetin; Wojciech Pieczynski (Traitement du Signal, 2014)

A Class of Fast Exact Bayesian Filters in Dynamical Models With Jumps. - Yohan Petetin; François Desbouvries (IEEE Trans. Signal Process., 2014)

Deep neural networks for audio scene recognition. - Yohan Petetin; Cyrille Laroche; Aurélien Mayoue (EUSIPCO, 2015)

Bayesian Conditional Monte Carlo Algorithms for Nonlinear Time-Series State Estimation. - Yohan Petetin; François Desbouvries (IEEE Trans. Signal Process., 2015)

Particle filters with independent resampling. - Roland Lamberti; Yohan Petetin; François Septier; François Desbouvries (ICASSP, 2016)

An improved SIR-based Sequential Monte Carlo algorithm. - Roland Lamberti; Yohan Petetin; François Septier; François Desbouvries (SSP, 2016)

Predictive Models of Hard Drive Failures Based on Operational Data. - Nicolas Aussel; Samuel Jaulin; Guillaume Gandon; Yohan Petetin; Eriza Fazli; Sophie Chabridon (ICMLA, 2017)

Independent Resampling Sequential Monte Carlo Algorithms. - Roland Lamberti; Yohan Petetin; François Desbouvries; François Septier (IEEE Trans. Signal Process., 2017)

Improving Performances of Log Mining for Anomaly Prediction Through NLP-Based Log Parsing. - Nicolas Aussel; Yohan Petetin; Sophie Chabridon (MASCOTS, 2018)

A Double Proposal Normalized Importance Sampling Estimator. - Roland Lamberti; Yohan Petetin; François Septier; François Desbouvries (SSP, 2018)

Semi-Independent Resampling for Particle Filtering. - Roland Lamberti; Yohan Petetin; François Desbouvries; François Septier (IEEE Signal Process. Lett., 2018)

Comparing the Modeling Powers of RNN and HMM. - Achille Salaün; Yohan Petetin; François Desbouvries (ICMLA, 2019)

Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics. - Nicolas Aussel; Sophie Chabridon; Yohan Petetin (CoRR, 2020)

A General Parametrization Framework for Pairwise Markov Models: An Application to Unsupervised Image Segmentation. - Hugo Gangloff; Katherine Morales; Yohan Petetin (MLSP, 2021)

Variational Bayesian Inference for Pairwise Markov Models. - Katherine Morales; Yohan Petetin (SSP, 2021)

Structured Variational Bayesian Inference for Gaussian State-Space Models With Regime Switching. - Yohan Petetin; Yazid Janati; François Desbouvries (IEEE Signal Process. Lett., 2021)

A Probabilistic Semi-Supervised Approach with Triplet Markov Chains. - Katherine Morales; Yohan Petetin (MLSP, 2023)

Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks From a System Theoretic Viewpoint. - François Desbouvries; Yohan Petetin; Achille Salaün (IEEE Trans. Signal Process., 2023)

Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data. - Hugo Gangloff; Katherine Morales; Yohan Petetin (Comput. Stat. Data Anal., 2023)

An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection. - Idyano Leroy; Augustin A. Saucan; Yohan Petetin; Daniel Clark (FUSION, 2024)