- FR
- EN
stoehr@ceremade.dauphine.fr
Phone
: 4967
Office
: B640
Personal URL
Julien Stoehr has been a lecturer at Université Paris-Dauphine since 2017. After graduating from the École Normale Supérieure Rennes (former École Normale Supérieure de Cachan - Antenne de Bretagne) and receiving an external degree (agrégation) in Mathematics, he got a PhD in statistics and specialized in the field of computational statistics. His research focuses on the design and implementation of statistical methodologies in a Bayesian setting where the statistical model is complex, e.g., likelihood with no closed form or generative models that are time consuming to simulate from, with a particular interest for the Monte Carlo methods (Markov chain Monte Carlo, Hamiltonian Monte Carlo and approximate Bayesian methods (ABC)).
Clarte G., Robert C., Ryder R., Stoehr J. (2021), Component-wise approximate Bayesian computation via Gibbs-like steps, Biometrika, vol. 108, n°3, p. 591–607 
Stoehr J., Benson A., Friel N. (2018), Noisy Hamiltonian Monte Carlo for Doubly Intractable Distributions, Journal of Computational and Graphical Statistics, vol. 28, n°1, p. 220-232
Stoehr J., Marin J-M., Pudlo P. (2016), Hidden Gibbs random fields model selection using Block Likelihood Information Criterion, Stat, vol. 5, n°1, p. 158-172
Stoehr J., Pudlo P., Cucala L. (2015), Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields, Statistics and Computing, vol. 25, n°1, p. 129-141
Stoehr J., Friel N. (2015), Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields, in , Volume 38: Artificial Intelligence and Statistics, 9-12 May 2015, San Diego, California, USA, IEEE - Institute of Electrical and Electronics Engineers, 921-929 p.
Stoehr J., Robin S. (2024), Composite likelihood inference for the Poisson log-normal model, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 32 p.
Stoehr J., Robert C. (2024), Simulating signed mixtures, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 30 p.
Clarte G., Ryder R., Robert C., Stoehr J. (2019), Component-wise approximate Bayesian computation via Gibbs-like steps, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 30 p.
Stoehr J. (2019), A review on statistical inference methods for discrete Markov random fields, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 31 p.
Wu C., Stoehr J., Robert C. (2019), Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 18 p.
