Pierre Pudlo
Pierre Pudlo
Aix-Marseille University; Institut de Mathématiques de Marseille (I2M)
Verified email at univ-amu.fr - Homepage
TitleCited byYear
Approximate Bayesian computational methods
JM Marin, P Pudlo, CP Robert, RJ Ryder
Statistics and Computing 22 (6), 1167-1180, 2012
5952012
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
5452014
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
1992013
Reliable ABC model choice via random forests
P Pudlo, JM Marin, A Estoup, JM Cornuet, M Gautier, CP Robert
Bioinformatics 32 (6), 859-866, 2015
1382015
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
1202013
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
912012
Bayesian computation via empirical likelihood
KL Mengersen, P Pudlo, CP Robert
Proceedings of the National Academy of Sciences 110 (4), 1321-1326, 2013
622013
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
552017
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
532014
ABC random forests for Bayesian parameter inference
L Raynal, JM Marin, P Pudlo, M Ribatet, CP Robert, A Estoup
Bioinformatics 35 (10), 1720-1728, 2018
41*2018
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
28*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
262012
Consistency of the adaptive multiple importance sampling
JM Marin, P Pudlo, M Sedki
arXiv preprint arXiv:1211.2548, 2012
252012
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
17*2015
Operator norm convergence of spectral clustering on level sets
B Pelletier, P Pudlo
Journal of Machine Learning Research 12 (Feb), 385-416, 2011
15*2011
Efficient learning in ABC algorithms
M Sedki, P Pudlo, JM Marin, CP Robert, JM Cornuet
arXiv preprint arXiv:1210.1388, 2012
142012
Bayesian functional linear regression with sparse step functions
PM Grollemund, C Abraham, M Baragatti, P Pudlo
Bayesian Analysis 14 (1), 111-135, 2019
122019
An overview on approximate Bayesian computation
M Baragatti, P Pudlo
ESAIM: Proceedings 44, 291-299, 2014
112014
Contribution to the discussion of Fearnhead and Prangle (2012). Constructing summary statistics for approximate Bayesian computation: Semi-automatic approximate Bayesian …
MA Sedki, P Pudlo
Journal of the Royal Statistical Society: Series B 74, 466-467, 2012
8*2012
Likelihood-free model choice
JM Marin, P Pudlo, A Estoup, C Robert
Handbook of Approximate Bayesian Computation, 153-178, 2018
62018
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Articles 1–20