nicolas brunel
nicolas brunel
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Zitiert von
Zitiert von
Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference
M Quach, N Brunel, F d'Alché-Buc
Bioinformatics 23 (23), 3209-3216, 2007
Parameter estimation of ODE’s via nonparametric estimators
NJB Brunel
Electronic Journal of Statistics 2, 1242-1267, 2008
Unsupervised signal restoration using hidden Markov chains with copulas
N Brunel, W Pieczynski
Signal processing 85 (12), 2304-2315, 2005
Copulas in vectorial hidden Markov chains for multicomponent image segmentation
N Brunel, W Pieczynski, S Derrode
Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech …, 2005
Parametric estimation of ordinary differential equations with orthogonality conditions
NJB Brunel, Q Clairon, F d’Alché-Buc
Journal of the American Statistical Association 109 (505), 173-185, 2014
Modeling and unsupervised classification of multivariate hidden Markov chains with copulas
NJB Brunel, J Lapuyade-Lahorgue, W Pieczynski
IEEE Transactions on Automatic Control 55 (2), 338-349, 2009
Unsupervised signal restoration using copulas and pairwise Markov chains
N Brunel, W Pieczynski
IEEE Workshop on Statistical Signal Processing, 2003, 102-105, 2003
A tracking approach to parameter estimation in linear ordinary differential equations
NJB Brunel, Q Clairon
Electronic Journal of Statistics 9 (2), 2903-2949, 2015
Humans are able to self-paced constant running accelerations until exhaustion
V Billat, NJB Brunel, T Carbillet, S Labbé, A Samson
Physica A: Statistical Mechanics and its Applications 506, 290-304, 2018
Sur quelques extensions des chaînes de Markov cachées et couples. Applications à la segmentation non-supervisée de signaux radar.
N Brunel
Université Pierre et Marie Curie-Paris VI, 2005
Tracking for parameter and state estimation in possibly misspecified partially observed linear ordinary differential equations
Q Clairon, NJB Brunel
Journal of Statistical Planning and Inference 199, 188-206, 2019
Optimal control and additive perturbations help in estimating ill-posed and uncertain dynamical systems
Q Clairon, NJB Brunel
Journal of the American Statistical Association 113 (523), 1195-1209, 2018
Removing phase variability to extract a mean shape for juggling trajectories
NJB Brunel, J Park
Electronic Journal of Statistics 8 (2), 1848-1855, 2014
Doppler and polarimetric statistical segmentation for radar clutter map based on pairwise Markov chains
N Brunel, F Barbaresco
Proc. of IEEE RADAR, 10-8, 2007
Copulas in vectorial hidden Markov chains for multicomponent image classification
N Brunel, W Piezcynski, S Derrode
IEEE Internat. Conf. on Acoustics, Speech and Signal Processing, 2005
Estimation of parametric nonlinear odes for biological networks identification
F d'Alché-Buc, N Brunel
Learning and Inference in Computational Systems Biology, 61--96, 2009
Accurate and robust Shapley Values for explaining predictions and focusing on local important variables
SI Amoukou, NJB Brunel, T Salaün
arXiv preprint arXiv:2106.03820, 2021
The Shapley Value of coalition of variables provides better explanations
SI Amoukou, NJB Brunel, T Salaün
arXiv preprint arXiv:2103.13342, 2021
Constrained nonlinear and mixed effects differential equation models for dynamic cell polarity signaling
Z Xiao, N Brunel, Z Yang, X Cui
arXiv preprint arXiv:1605.00185, 2016
Chaînes de Markov cachées multivariées à bruit corrélé non gaussien, avec applications à la segmentation du signal radar
N Brunel, W Pieczynski, F Barbaresco
20° Colloque sur le traitement du signal et des images, FRA, 2005, 2005
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