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Journal Paper     (the available pdf files are close to the published version)
  1. S. Le Corff and G. Fort. Convergence of a particle-based approximation of the Block online  Expectation Maximization algorithm, Accepted in Transactions on Modeling and Computer Simulation, 2012.
  2. G. Fort, E. Moulines, P. Priouret and P. Vandekerkhove. A simple variance inequality  for U-statistics of a Markov chain, with applications. Accepted for publication, Stat. and Prob. Letters, 2012, arXiv math.ST 1107-2576
  3. G. Fort, E. Moulines and P. Priouret. Convergence of adaptive and interacting  Markov chain Monte Carlo algorithms. Ann. Statist. 39(6):3262-3289, 2012.
  4. Y. Atchadé and G. Fort. Limit theorems for some adaptive MCMC algorithms with subgeometric kernels, part II. Accepted in Bernoulli 2011.
  5. M. Kilbinger, D. Wraith, C. P. Robert, K. Benabed, O. Cappé,  J.F.Cardoso, G. Fort, S. Prunet, and F.R.Bouchet. Bayesian model comparison in cosmology with Population Monte Carlo.  MNRAS 405(4):2381-2390, 2010. ArXiv astro-ph.CO/0912.1614
  6. P. Etoré, G. Fort, B. Jourdain and E. Moulines. On adaptive stratification. Annals of Operations Research 189(1):127-154, 2011. ArXiv math.PR/0809.1135
  7. Y. Atchadé and G. Fort. Limit theorems for some adaptive MCMC algorithms with subgeometric kernels. Bernoulli 16(1):116-154, 2010. ArXiv  math.PR/0807.2952  
  8. S. Connor and G. Fort. State-dependent Foster-Lyapunov criteria for subgeometric convergence of Markov chains. Stochastic Processes Appl. 119:4176-4193, 2009  ArXiv  math.PR/0901.2453
  9. D. Wraith, M. Kilbinger, K. Benabed, O. Cappé,  J.F.Cardoso, G. Fort, S. Prunet and C. P. Robert. Estimation of cosmological parameters using adaptive importance sampling.  Phys.Rev. D. 80(2), 2009.  ArXiv stat.CO/0903.0837
  10. R. Douc, G. Fort, E. Moulines and P. Priouret. Forgetting of the initial distribution for Hidden Markov Models. Stoch. Process Appl, 119(4): 1235-1256, 2009.   ArXiv  math.ST/0703836 
  11. R. Douc, G. Fort and A. Guillin. Subgeometric rates of convergence of f-ergodic strong Markov processes.  Stoch. Process Appl, 119(3):897-923, 2009. ArXiv math.ST/0605791
  12. G. Fort, S. Meyn, E. Moulines and P. Priouret. The ODE method for the stability of skip-free Markov Chains with applications to MCMC. Ann. Appl. Probab. 18(2) :664-707, 2008.
  13. F. Forbes and G. Fort. A convergence theorem for Variational EM-like algorithms : application to image segmentation. IEEE Transactions on Image Processing, 16(3):824-837,2007  MATLAB Codes   
  14. G. Fort, S. Lambert-Lacroix, J. Peyre. Réduction de dimension dans les modèles généralisés : Application à la classification de données issues des biopuces. Journal de la SFDS, 146(1-2):117-152,2005.  Matlab codes and Data set.  Erratum on the research report TR0471
  15. G. Fort and S. Lambert-Lacroix. Classification using Partial Least Squares with Penalized Logistic Regression. Bioinformatics,  21(7):1104-1111, 2005.  Matlab codes and Data set.
  16. G. Fort and G. O. Roberts. Subgeometric ergodicity of strong Markov processes.  Ann. Appl. Probab. 15(2):1565-1589, 2005.
  17. R. Douc, G. Fort, E. Moulines and P. Soulier. Practical drift conditions for subgeometric rates of convergence.  Ann. Appl. Probab. 14(3) :1353-1377, 2004.
  18. G. Fort, E. Moulines, G.O. Roberts and J.S. Rosenthal.  On the geometric ergodicity of hybrid samplers.   J. Appl. Probab. 40(1):123-146, 2003.
  19. G. Fort and E. Moulines. Polynomial ergodicity of Markov transition kernels. Stochastic Processes Appl. 103(1):57-99, 2003. 
  20. G. Fort and E. Moulines. Convergence of the Monte-Carlo EM for curved exponential families.   Ann. Stat. 31(4):1220-1259, 2003. 
  21. G. Fort and E. Moulines. V-subgeometric ergodicity for a Hastings-Metropolis algorithm. Stat. Probab. Lett. 49(4):401-410,2000.
Chapters in books  
  1. Y. Atchadé, G. Fort, E. Moulines and P. Priouret.  In D. Barber, A. T. Cemgil and S. Chiappia, editors. Bayesian Time Series Models, Cambridge Univ. Press, 2011. Chapter 2 : Adaptive Markov chain Monte Carlo : Theory and Methods, 33-53.
  2. G. Fort, E. Moulines and P. Soulier. In O. Cappe, E. Moulines and T. Ryden, editors. Inference in Hidden Markov Models, Springer  2005. Chapter 14: Elements of Markov Chain Theory, 511-562.
Conference Proceedings
  1. R. Bardenet, O. Cappé, G. Fort and B. Kegl. Adaptive Metropolis with online relabeling. (Supplementary paper). JMLR Workshop and Conference Proceedings Vol 22, p.91-99, AISTATS 2012
  2. S. Le Corff, G. Fort and E. Moulines. New Online-EM algorithms for general Hidden Markov models. Application to the SLAM,LVA-ICA 2012, Springer pages131--138.
  3. P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Performance Analysis of a Distributed On-Line Estimator for Sensor Networks, 2011. Accepted, EUSIPCO 2011.
  4. S. Le Corff, G. Fort and E. Moulines. Un algorithme EM récursif pour le SLAM, 2011. Accepted, GRETSI 2011.
  5. P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Sur un algorithme de Robbins-Monro distribué, 2011. Accepted, GRETSI 2011.
  6. S. Le Corff, G. Fort and E. Moulines. Online Expectation-Maximization algorithm to solve the SLAM problem, 2011. Accepted, SSP 2011.
  7. S. Le Corff and G. Fort. Block Online EM for Hidden Markov Models with general state space, 2011. Accepted, ASMDA 2011.
  8. P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Convergence of a distributed parameter estimator for sensor network with local averaging of the estimates. Accepted, ICASSP 2011.
  9. G. Fort, S. Meyn, E. Moulines and P. Priouret. ODE methods for Markov chain stability with applications to MCMC. Proceedings of the 1st International Conference on Performance Evaluation Methodologies and Tools,  Valuetools, Art. 42, 2006.
  10. G. Fort and S. Lambert-Lacroix. Ridge-Partial Least Squares for Generalized Linear Models with binary response. COMPSTAT'04, Proceedings in Computational Statistics, pages 1019-1026, 2004. 
  11. G. Fort and E. Moulines, and P. Soulier. On the convergence of iterated random maps with applications to the MCEM algorithm. Computational Statistics, August, 1998.
  12. G. Fort, O. Cappé, E. Moulines, and P. Soulier. Optimization via simulation for maximum likelihood estimation in incomplete data models. In Proc. IEEE Workshop on Stat. Signal and Array Proc., pages 80-83, 1998.
Technical Report
  1. G. Fort. Central Limit Theorems for stochastic approximation algorithms. March 2012
  2. P. Bianchi, G. Fort and W. Hachem.  Performance of a Distributed Stochastic Approximation Algorithm, Submitted, March 2012.
  3. A. Schreck, G. Fort and E. Moulines. Adaptive Equi-energy sampler : convergence and illustration. Submitted, October 2011. Revised in March 2012.
  4. S. Le Corff and G. Fort. Online Expectation Maximization-based algorithms for inference in Hidden Markov Models. Submitted, August 2011, arXiv math.ST 1108-3968. Supplement paper, math.ST 1108-4130. Revised in Jan 2012
  5. G. Fort, E. Moulines, P. Priouret and P. Vandekerkhove. A Central  Limit Theorem for Adaptive and Interacting Markov Chains. Submitted, July 2011, arXiv math.ST 1107-2574 Supplement paper   Revised in Nov 2011.
  6. G. Fort. Fluid limit-based tuning of some hybrid MCMC samplers. Dec 2007.
  7. C. Andrieu and G. Fort. Explicit control of subgeometric ergodicity. Rapport de Recherche, 05:17, 2005.
  8. G. Fort. Partial Least Squares for classification and feature selection in Microarray gene expression data. Dec. 2004.
  9. G. Fort. Computable bounds for V-geometric ergodicity of Markov transition kernels. Rapport de Recherche, Univ. J. Fourier, RR 1047-M.
Works in progress
  1. with A. Schreck, A. Garivier, E. Moulines and M. Vihola - about  interacting and tempering Monte Carlo algorithms.
  2. with B. Miazojedow - about interacting methods for Particle filtering.
  3. with R. Bardenet, O.Cappé, B. Kegl - about a new MCMC sampler robust to the label-switching problem, with applications to statistical signal processing of Auger experiments.
  4. with B. Jourdain,  T. Lelièvre, G. Stoltz - about theoretical properties of Wang-Landau types algorithms.
PhD Thesis and HDR
  1. G. Fort. Habilitation à Diriger les Recherches "Méthodes de Monte Carlo et Chaînes de Markov pour la simulation". Univ. Paris Dauphine, Feb. 2010. (website)
  2. G. Fort. PhD thesis. "Contrôle explicite d'ergodicité de chaînes de Markov : application à l'analyse de convergence de l'algorithme  Monte Carlo EM". Univ. Paris VI, June 2001. Inist Number : T139824