Number of documents

8

Julien Stoehr - Maître de Conférence in Statistics


Journal articles3 documents

  • Julien Stoehr, Alan Benson, Nial Friel. Noisy Hamiltonian Monte Carlo for Doubly Intractable Distributions. Journal of Computational and Graphical Statistics, Taylor & Francis, 2018, pp.1-13. ⟨hal-01969085⟩
  • Stoehr Julien, Jean-Michel Marin, Pierre Pudlo. Hidden Gibbs random fields model selection using Block Likelihood Information Criterion. Stat, John Wiley & Sons, 2016, 5 (1), pp.158-172. ⟨10.1002/sta4.112⟩. ⟨hal-01330202⟩
  • Julien Stoehr, Pierre Pudlo, Lionel Cucala. Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields. Statistics and Computing, Springer Verlag (Germany), 2014, ⟨10.1007/s11222-014-9514-9⟩. ⟨hal-00942797v2⟩

Directions of work or proceedings1 document

  • Julien Stoehr, Nial Friel. Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields. The Eighteenth International Conference on Artificial Intelligence and Statistics, May 2015, San Diego, United States. Proceedings of Machine Learning Research, 38, pp.921-929, 2015. ⟨hal-01108279v2⟩

Preprints, Working Papers, ...3 documents

  • Changye Wu, Julien Stoehr, Christian Robert. Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale. 2019. ⟨hal-01968770⟩
  • Grégoire Clarté, Christian P. Robert, Robin Ryder, Julien Stoehr. Component-wise approximate Bayesian computation via Gibbs-like steps. 2019. ⟨hal-02274914⟩
  • Julien Stoehr, Richard Everitt, Matthew T. Moores. A review on statistical inference methods for discrete Markov random fields. 2017. ⟨hal-01462078v2⟩

Theses1 document