Submitted papers

  1. F. Maire (2018) Dereversibilizing Metropolis-Hastings: simple implementation of non-reversible MCMC methods. pdf

  2. F. Maire. and P. Vanderkerkhove (2018) On Markov chain Monte Carlo for sparse and filamentary distributions. arXiv:1806.09000

  3. M. Vialaret and F. Maire (2018) Note on the convergence time of some non-reversible Markov chain Monte Carlo methods. arXiv:1807.02614

  4. R. Rastelli, N. Friel and F. Maire (2018) Computationally efficient inference for latent position network models. arXiv:1804.02274

  5. A. Boland, N. Friel and F. Maire (2018) Efficient MCMC for Gibbs Random Fields using pre-computation. arXiv:1710.04093

  6. F. Maire, N. Friel, A. Mira and A. Raftery (2017) Adaptive Incremental Mixture Markov chain Monte Carlo. arXiv:1604.08016



    7. Publications

  7. F. Maire., N. Friel and P. Alquier (2018) Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets. Statistics and Computing arXiv:1706.08327 , doi.org/10.1007/s11222-018-9817-3

  8. L. Bouranis, N. Friel and F. Maire. (2018) Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo. Computational Statistics & Data Analysis vol. 128: 221-241. arXiv:1712.05358 , doi.org/10.1016/j.csda.2018.07.005

  9. L. Bouranis, N. Friel and F. Maire. (2018) Bayesian model selection for exponential random graph models via adjusted pseudolikelihood. Journal of Computational and Graphical Statistics vol. 27 (3): 516-528. arXiv:1706.06344 , doi.org/10.1080/10618600.2018.1448832

  10. F. Maire, E. Moulines and S. Lefebvre. (2017) Online EM for Functional Data. Computational Statistics & Data Analysis vol. 111: 27-47. arXiv:1604.00570 , doi.org/10.1016/j.csda.2017.01.006

  11. L. Bouranis, N. Friel and F. Maire. (2017) Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution. Social Networks vol.50: 98-108. arXiv:1510.00934 , doi.org/10.1016/j.socnet.2017.03.013

  12. F. Maire and S. Lefebvre. (2015) Detecting aircraft in low-resolution multispectral images: specification of relevant IR wavelength bands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi.2015.2457514

  13. R. Douc, F. Maire and J. Olsson. (2015) On the use of Markov chain Monte Carlo methods for the sampling of mixture models: a statistical perspective. Statistics and Computing vol. 25(1), 95-110. arXiv:1404.0880

  14. H. I. Brahmi, F. Maire, S. Djahel, and J. Murphy. (2015) Planning & Acting: Optimal Markov Decision Scheduling of Aggregated Data in WSNs by Genetic Algorithm. IEEE PIMRC 2015. doi.2015.7343638

  15. F. Maire, R. Douc and J. Olsson. (2014) Comparison of asymptotic variances of inhomogeneous Markov chains with application to Markov chain Monte Carlo methods. The Annals of Statistics. vol. 42(4) 1483-1510. arXiv:1307.3719

  16. S. Allassonnière, J. A. Glaunès, J. Bigot, F. Maire and F. J-P. Richard. (2013) Statistical models for deformable templates in image and shape analysis. Annales Mathématiques Blaise Pascal.doi.ambp.320

  17. F. Maire, S. Lefebvre, R. Douc and É. Moulines. (2012) An online learning algorithm for mixture models of deformable templates. Proceedings of IEEE Machine Learning for Signal Processing workshop. 1--6. doi.2012.6349725

  18. J. Jakubowicz, S. Lefebvre, F. Maire. and É. Moulines. (2012) Detecting Aircraft With a Low-Resolution Infrared Sensor. IEEE Transactions on Image Processing. vol 21, 3034--3041. doi.2012.2186307

  19. F. Maire, S. Lefebvre, R. Douc and É. Moulines. (2011) Aircraft classification with low infrared sensor. Proceedings of IEEE Statistical Signal Processing workshop. 761-765. doi.2011.5967815