| DIYABC v2. 0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data JM Cornuet, P Pudlo, J Veyssier, A Dehne-Garcia, M Gautier, R Leblois, ... Bioinformatics 30 (8), 1187-1189, 2014 | 1120 | 2014 |
| Approximate Bayesian computational methods JM Marin, P Pudlo, CP Robert, RJ Ryder Statistics and computing 22 (6), 1167-1180, 2012 | 1028 | 2012 |
| Reliable ABC model choice via random forests P Pudlo, JM Marin, A Estoup, JM Cornuet, M Gautier, CP Robert Bioinformatics 32 (6), 859-866, 2016 | 421 | 2016 |
| The effect of RAD allele dropout on the estimation of genetic variation within and between populations M Gautier, K Gharbi, T Cezard, J Foucaud, C Kerdelhué, P Pudlo, ... Molecular ecology 22 (11), 3165-3178, 2013 | 333 | 2013 |
| Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping M Gautier, J Foucaud, K Gharbi, T Cézard, M Galan, A Loiseau, ... Molecular Ecology 22 (14), 3766-3779, 2013 | 250 | 2013 |
| ABC random forests for Bayesian parameter inference L Raynal, JM Marin, P Pudlo, M Ribatet, CP Robert, A Estoup Bioinformatics 35 (10), 1720-1728, 2019 | 222 | 2019 |
| Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest A Fraimout, V Debat, S Fellous, RA Hufbauer, J Foucaud, P Pudlo, ... Molecular biology and evolution 34 (4), 980-996, 2017 | 180 | 2017 |
| Estimation of demo‐genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics A Estoup, E Lombaert, JM Marin, T Guillemaud, P Pudlo, CP Robert, ... Molecular Ecology Resources 12 (5), 846-855, 2012 | 134 | 2012 |
| Bayesian computation via empirical likelihood KL Mengersen, P Pudlo, CP Robert Proceedings of the National Academy of Sciences 110 (4), 1321-1326, 2013 | 104 | 2013 |
| Maximum-likelihood inference of population size contractions from microsatellite data R Leblois, P Pudlo, J Néron, F Bertaux, C Reddy Beeravolu, R Vitalis, ... Molecular biology and evolution 31 (10), 2805-2823, 2014 | 79 | 2014 |
| Consistency of adaptive importance sampling and recycling schemes JM Marin, P Pudlo, M Sedki | 56* | 2019 |
| Estimation of density level sets with a given probability content B Cadre, B Pelletier, P Pudlo Journal of Nonparametric Statistics 25 (1), 261-272, 2013 | 46* | 2013 |
| Bayesian functional linear regression with sparse step functions PM Grollemund, C Abraham, M Baragatti, P Pudlo | 39 | 2019 |
| Likelihood-free model choice JM Marin, P Pudlo, A Estoup, C Robert Handbook of approximate Bayesian computation, 153-178, 2018 | 38 | 2018 |
| Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer J Guillot, C Dominici, A Lucchesi, HTT Nguyen, A Puget, M Hocine, ... Nature communications 13 (1), 1985, 2022 | 37 | 2022 |
| The normalized graph cut and Cheeger constant: from discrete to continuous E Arias-Castro, B Pelletier, P Pudlo Advances in Applied Probability 44 (4), 907-937, 2012 | 33 | 2012 |
| Constraining the recent star formation history of galaxies: an approximate Bayesian computation approach G Aufort, L Ciesla, P Pudlo, V Buat Astronomy & Astrophysics 635, A136, 2020 | 23 | 2020 |
| Operator norm convergence of spectral clustering on level sets B Pelletier, P Pudlo The Journal of Machine Learning Research 12, 385-416, 2011 | 22* | 2011 |
| An overview on approximate Bayesian computation M Baragatti, P Pudlo ESAIM: Proceedings 44, 291-299, 2014 | 21 | 2014 |
| 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 | 18* | 2015 |
Jean-Michel MarinProfessor of Statistics, University of MontpellierEmail verificata su umontpellier.fr
