Componentwise approximate Bayesian computation via Gibbs-like steps G Clarté, CP Robert, RJ Ryder, J Stoehr Biometrika 108 (3), 591-607, 2021 | 22 | 2021 |
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields J Stoehr, P Pudlo, L Cucala Statistics and Computing 25, 129-141, 2015 | 21* | 2015 |
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields J Stoehr, N Friel Artificial Intelligence and Statistics, 921-929, 2015 | 20 | 2015 |
A review on statistical inference methods for discrete Markov random fields J Stoehr arXiv preprint arXiv:1704.03331, 2017 | 18 | 2017 |
Faster Hamiltonian Monte Carlo by learning leapfrog scale C Wu, J Stoehr, CP Robert arXiv preprint arXiv:1810.04449, 2018 | 16 | 2018 |
Noisy Hamiltonian Monte Carlo for doubly intractable distributions J Stoehr, A Benson, N Friel Journal of Computational and Graphical Statistics 28 (1), 220-232, 2019 | 11 | 2019 |
Hidden Gibbs random fields model selection using block likelihood information criterion J Stoehr, JM Marin, P Pudlo Stat 5 (1), 158-172, 2016 | 8 | 2016 |
GiRaF: a toolbox for Gibbs Random Fields analysis J Stoehr, P Pudlo, N Friel R package version 1 (1), 2020 | 4 | 2020 |
Statistical inférence methods for Gibbs random fields J Stoehr Université de Montpellier, 2015 | 3 | 2015 |
Méthodes d'inférence statistique pour champs de Gibbs J Stoehr Montpellier, 2015 | | 2015 |
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields L Cucala, J Stoehr, P Pudlo HAL 2015, 2015 | | 2015 |
Component-wise Approximate Bayesian Computation via Gibbs-like steps CPR GRegoire CLARTe, RJ RYDER, J STOEHR | | |
A toolbox for Gibbs Random Fields analysis J Stoehr, P Pudlo, N Friel | | |
Criteres de choix de modele pour champs de Gibbs cachés J Stoehr, JM Marin, P Pudlo | | |
Statistiques résumées géométriques pour le choix de modele ABC entre des champs de Gibbs cachés J Stoehr, P Pudlo, L Cucala | | |