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MIA Paris

Pierre Barbillon

Assistant Professor / Maître de conférence

UMR518 AgroParisTech/INRA
Team MORSE
16 rue Claude Bernard
75 231 Paris Cedex 05
Tel : +33 (0)1 44 08 86 82
pierre.barbillon@agroparistech.fr

Research

  • Designs and analysis of computer experiments.
  • Stochastic algorithms (MCMC, simulated annealing, stochastic EM).
  • Inverse Problems, Rare Events.
  • Social networks.
  • MIRES (Méthodes Interdisciplinaires d'Échanges de Semences) research group.

Publications (selected)

  • Courbariaux, M., Barbillon, P., & Parent, É. Water flow probabilistic predictions based on a rainfall–runoff simulator: a two-regime model with variable selection. Journal of Agricultural, Biological and Environmental Statistics, 1-26. (online)
  • Damblin, G., Keller, M., Barbillon, P., Pasanisi, A., et Parent, É. (2016). Bayesian Model Selection for the Validation of Computer Codes. Quality and Reliability Engineering International. (online)
  • Lazega, E., Bar-Hen, A., Barbillon, P. et Donnet, S. (2016). Effects of competition on collective learning in advice networks. Social Networks. (online)
  • Barbillon, P., Barthélémy, C., et Samson, A. (2016). Parameter estimation of complex mixed models based on meta-model approach. Statistics & Computing. (online)
  • Barbillon, P., Donnet, S., Lazega, E. et Bar-Hen, A. (2016). Stochastic block models for multiplex networks: an application to a multilevel network of researchers. Journal of the Royal Statistical Society, Series A. (arXiv)  (online)
  • Thomas, M., Verzelen, N., Barbillon, P. et al. (2015). A Network-Based Method to Detect Patterns of Local Crop Biodiversity: Validation at the Species and Infra-Species Levels. Advances in Ecological Research. (online)
  • Barbillon, P., Thomas, M., Goldringer, I., Hospital, F. et Robin, S. (2015). Network impact on persistence in a finite population dynamic diffusion model: application to an emergent seed exchange network. Journal of Theoretical Biology, pp. 365-376. (arXiv) (online)
  • Auffray, Y., Barbillon, P., Marin, J.-M. (2014). Bounding rare event probabilities in computer experiments. Computational Statistics & Data Analysis, 80 : 153–166 (online)
  • Damblin, G., Keller, M., Pasanisi, A., Barbillon, P. et Parent, É. (2014). Approche décisionnelle bayésienne pour estimer une courbe de fragilité. Journal de la Société Française de Statistique, 155(3) : 78-103 (online) . 
  • Auffray, Y., Barbillon, P., Marin, J.-M. (2011). Maximin Design on non-hypercube domain and Kernel Interpolation, Statistics and Computing. (online)
  • Auffray, Y., Barbillon, P., Marin, J.-M. (2011). Modèles réduits à partir d'expérience numériques, Journal de la Société Française de Statistique, 152(1), 89-102. (online)
  • Barbillon, P., Celeux, G., Grimaud, A., Lefebvre, Y., Rocquigny (De), E. (2011). Non linear methods for inverse statistical problems. Computational Statistics & Data Analysis, 55 (1), 132-142. (online)

Pré-publications :

  • Damblin, G., Barbillon, P., Keller, M., Pasanisi, A. et Parent, É (2015). Adaptive numerical designs for the calibration of computer codes. (arXiv).
  • Auffray, Y. and Barbillon, P. (2009). Conditionally positive definite kernels: theoritical contribution, application to interpolation and approximation. Technical report, Rapport de recherche INRIA. (hal)

Teaching