Pierre Pudlo
Pierre Pudlo
Aix-Marseille University; Institut de Mathématiques de Marseille (I2M)
Verified email at univ-amu.fr - Homepage
Title
Cited by
Cited by
Year
Approximate Bayesian computational methods
JM Marin, P Pudlo, CP Robert, RJ Ryder
Statistics and Computing 22 (6), 1167-1180, 2012
6802012
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
6412014
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
2212013
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
1672016
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
1402013
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
1012012
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
742017
Bayesian computation via empirical likelihood
KL Mengersen, P Pudlo, CP Robert
Proceedings of the National Academy of Sciences 110 (4), 1321-1326, 2013
722013
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
622019
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
572014
Consistency of adaptive importance sampling and recycling schemes
JM Marin, P Pudlo, M Sedki
Bernoulli 25 (3), 1977-1998, 2019
30*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
30*2013
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
282012
Likelihood-free model choice
JM Marin, P Pudlo, A Estoup, C Robert
Handbook of Approximate Bayesian Computation, 153, 2018
182018
Operator norm convergence of spectral clustering on level sets
B Pelletier, P Pudlo
The Journal of Machine Learning Research 12, 385-416, 2011
17*2011
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields
J Stoehr, P Pudlo, L Cucala
Statistics and Computing 25 (1), 129-141, 2015
16*2015
ABC model choice via random forests
P Pudlo, JM Marin, A Estoup, JM Cornuet, M Gautier, CP Robert
ArXiv e-prints 1406, v2, 2014
162014
Bayesian functional linear regression with sparse step functions
PM Grollemund, C Abraham, M Baragatti, P Pudlo
Bayesian Analysis 14 (1), 111-135, 2019
142019
Efficient learning in ABC algorithms
M Sedki, P Pudlo, JM Marin, CP Robert, JM Cornuet
arXiv preprint arXiv:1210.1388, 2012
142012
An overview on approximate Bayesian computation
M Baragatti, P Pudlo
ESAIM: Proceedings 44, 291-299, 2014
122014
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Articles 1–20