Gersende Fort
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Classification using partial least squares with penalized logistic regression
G Fort, S Lambert-Lacroix
Bioinformatics 21 (7), 1104-1111, 2005
Convergence of the Monte Carlo expectation maximization for curved exponential families
G Fort, E Moulines
The Annals of Statistics 31 (4), 1220-1259, 2003
Practical drift conditions for subgeometric rates of convergence
R Douc, G Fort, E Moulines, P Soulier
The Annals of Applied Probability 14 (3), 1353-1377, 2004
Subgeometric rates of convergence of f-ergodic strong Markov processes
R Douc, G Fort, A Guillin
Stochastic processes and their applications 119 (3), 897-923, 2009
Performance of a distributed stochastic approximation algorithm
P Bianchi, G Fort, W Hachem
IEEE Transactions on Information Theory 59 (11), 7405-7418, 2013
Estimation of cosmological parameters using adaptive importance sampling
D Wraith, M Kilbinger, K Benabed, O Cappé, JF Cardoso, G Fort, S Prunet, ...
Physical Review D 80 (2), 023507, 2009
Convergence of adaptive and interacting Markov chain Monte Carlo algorithms
G Fort, E Moulines, P Priouret
The Annals of Statistics, 3262-3289, 2011
Adaptive Markov chain Monte Carlo: theory and methods
Y Atchade, G Fort, E Moulines, P Priouret
Bayesian time series models 1, 2011
Polynomial ergodicity of Markov transition kernels
G Fort, E Moulines
Stochastic Processes and their Applications 103 (1), 57-99, 2003
Subgeometric ergodicity of strong Markov processes
G Fort, GO Roberts
The Annals of Applied Probability 15 (2), 1565-1589, 2005
Bayesian model comparison in cosmology with Population Monte Carlo
M Kilbinger, D Wraith, CP Robert, K Benabed, O Cappé, JF Cardoso, ...
Monthly Notices of the Royal Astronomical Society 405 (4), 2381-2390, 2010
Forgetting the initial distribution for hidden Markov models
R Douc, G Fort, E Moulines, P Priouret
Stochastic processes and their applications 119 (4), 1235-1256, 2009
On perturbed proximal gradient algorithms
YF Atchadé, G Fort, E Moulines
The Journal of Machine Learning Research 18 (1), 310-342, 2017
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels
Y Atchadé, G Fort
Bernoulli 16 (1), 116-154, 2010
V-subgeometric ergodicity for a Hastings–Metropolis algorithm
G Fort, E Moulines
Statistics & probability letters 49 (4), 401-410, 2000
On the geometric ergodicity of hybrid samplers
G Fort, E Moulines, GO Roberts, JS Rosenthal
Journal of Applied Probability 40 (1), 123-146, 2003
On stochastic proximal gradient algorithms
YF Atchade, G Fort, E Moulines
arXiv preprint arXiv:1402.2365 237, 2014
Combining Monte Carlo and mean-field-like methods for inference in hidden Markov random fields
F Forbes, G Fort
IEEE transactions on image processing 16 (3), 824-837, 2007
Convergence of the Wang-Landau algorithm
G Fort, B Jourdain, E Kuhn, T Lelièvre, G Stoltz
Mathematics of Computation 84 (295), 2297-2327, 2015
A shrinkage-thresholding Metropolis adjusted Langevin algorithm for Bayesian variable selection
A Schreck, G Fort, S Le Corff, E Moulines
IEEE Journal of Selected Topics in Signal Processing 10 (2), 366-375, 2015
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