Update: March 2021

Bayesian statistics      Model choice

Approximate Bayesian Computation      Importance sampling schemes

Graphical and mixture models      Computer experiments

Population genetics


DIYABC Random Forest (version 1.0, January 2021) Approximate Bayesian Computation via Random Forests, for model choice and parameter inference in the context of population genetics analysis

R library abcrf   (version 1.8.1, October 2019) Approximate Bayesian Computation via Random Forests

DIYABC   (version 2.1.0, July 2015) a user-friendly approach to Approximate Bayesian Computation for inference on population history using molecular markers


Marin and Robert (2014) Bayesian Essentials with R, Springer Texts in Statistics, Springer Verlag, New York

Marin and Robert (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics, Springer Texts in Statistics, Springer Verlag, New York

Papers in refereed journal

Collin, Raynal, Durif, Gautier, Vitalis, Lombaert, Marin and Estoup (2021) DIYABC Random Forest v1.0 : extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms, Molecular Ecology Resources

Chapuis, Raynal, Plantamp, Meynard, Blondin, Marin and Estoup (2020) A young age of subspecific divergence in the Desert locust Schistocerca gregaria, inferred by ABC Random Forest, Molecular Ecology, 29(23), 4542-4558

Marin, Pudlo and Sedki (2019) Consistency of Adaptive Importance Sampling and Recycling Schemes, Bernoulli, 25(3), 1977-1998

Raynal, Marin, Pudlo, Ribatet, Robert and Estoup (2019) ABC random forests for Bayesian parameter inference, Bioinformatics, 35(10), 1720-1728

Bessière, Taha, Petitprez, Vandel, Marin, Bréhélin, Lèbre and Lecellier (2018) Probing instructions for expression regulation in gene nucleotide compositions, PLOS Computational Biology

Estoup, Raynal, Verdu, Marin (2018) Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations, Journal de la Société Française de Statistique, 159(3), 167-190

Fraimout, Debat, Fellous, Hufbauer, Foucaud, Pudlo, Marin, Price, Cattel, Chen, Deprá, Duyck, Guedot, Kenis, Kimura, Loeb, Loiseau, Martinez-Sañudo, Pascual, Polihronakis, Richmond, Shearer, Singh, Tamura, Xuéreb, Zhang and Estoup (2017) Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest, Molecular Biology and Evolution, 34 (4), 980–996

Stoehr, Marin and Pudlo (2016) Hidden Gibbs random fields model selection using Block Likelihood Information Criterion, Stat, 5(1), 158–172

Pudlo, Marin, Cornuet, Estoup, Gautier and Robert (2016) Reliable ABC model choice via random forests, Bioinformatics, 32(6), 859-866

Auffray, Barbillon and Marin (2014) Bounding rare event probabilities in computer experiments, Computational Statistics and Data Analysis, 80, 153-166

Cornuet, Pudlo, Veyssier, Dehne-Garcia, Gautier, Leblois, Marin and Estoup (2014) DIYABC v2.0: a software to make Approximate Bayesian Computation inferences about population history using Single Nucleotide Polymorphism, DNA sequence and microsatellite data, Bioinformatics, 30(8), 1187-1189

Marin, Pillai, Robert and Rousseau (2014) Relevant statistics for Bayesian model choice, Journal of the Royal Statistical Society, Series B, 76(5), 833-859

Cucala and Marin (2013) Bayesian Inference on a Mixture Model With Spatial Dependence, Journal of Computational and Graphical Statistics, 22(3), 584-597

Auffray, Barbillon and Marin (2012) Maximin design on non hypercube domain and kernel interpolation, Statistics and Computing, 22(3), 703-712

Besnard, Babled, Lapasset, Milhavet, Parrinello, Dantec, Marin and Lemaitre (2012) Unraveling cell type–specific and reprogrammable human replication origin signatures associated with G-quadruplex consensus motifs, Nature Structural and Molecular Biology, July 1

Celeux, El Anbari, Marin and Robert (2012) Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation, Bayesian Analysis, 7(2), 477-502

Cornuet, Marin, Mira and Robert (2012) Adaptive Multiple Importance Sampling, Scandinavian Journal of Statistics, 39(4), 798-812

Donnet and Marin (2012) An empirical Bayes procedure for the selection of Gaussian graphical models, Statistics and Computing, 22(5), 1113-1123

Estoup, Lombaert, Marin, Guillemaud, Pudlo, Robert and Cornuet (2012) Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics, Molecular Ecology Resources, 12(5), 846–855

Marin, Pudlo, Robert and Ryder (2012) Approximate Bayesian Computation methods, Statistics and Computing, 22(6), 1167-1180

Auffray, Barbillon and Marin (2011) Modeles reduits a partir d'experience numeriques, Journal de Societe Française de Statistique, 152(1), 89-102

Robert, Cornuet, Marin and Pillai (2011) Lack of confidence in approximate Bayesian computation model choice, Proceedings of the National Academy of Science, 108(37), 15112-15117

Iacobucci, Marin and Robert (2010) On variance stabilisation by double Rao-Blackwellisation, Computational Statistics and Data Analysis, 54, 698-710

Jouini, Marin and Napp (2010) Discounting and Divergence of Opinion, Journal of Economic Theory, 145(2), 830-859

Marin and Robert (2010) On resolving the Savage–Dickey paradox, Electronic Journal of Statistics, 4, 643-654

Beaumont, Cornuet, Marin and Robert (2009) Adaptive approximate Bayesian computation, Biometrika, 96(4), 983-990`

Casarin and Marin (2009) Online data processing: Comparison of Bayesian regularized particle filters, Electronic Journal of Statistics, 3, 239-258

Cucala, Marin, Robert, and Titterington (2009) A Bayesian reassessment of nearest-neighbour classification, Journal of the American Statistical Association, Theory and Methods, March 1, 104(485), 263-273

Grelaud, Robert, Marin, Rodolphe and Taly (2009) ABC likelihood-free methods for model choice in Gibbs random fields, Bayesian Analysis, 4(2), 317-336

Grelaud, Robert and Marin (2009) ABC methods for model choice in Gibbs random fields, Compte Rendus Academie des Sciences - Paris, Ser. I, 347, 205–210

Ben Mansour, Jouini, Marin, Napp and Robert (2008) Are risk agents more optimistic? A Bayesian estimation approach, Journal of Applied Econometrics, 23, 843-860

Cappe, Douc, Gullin, Marin and Robert (2008) Adaptive Importance Sampling in General Mixture Classes, Statistics and Computing, 18,  447-459

Cornuet, Santos, Beaumont, Robert, Marin, Balding, Guillemaud and Estoup (2008) Infering population history with DIY ABC: a user-friedly approach Approximate Bayesian Computation, Bioinformatics, 24(23), 2713-2719

Marin and Robert (2008) Approximating the marginal likelihood in mixture models, Indian Bayesian Society News Letter, V(1), 2-7

Robert and Marin (2008) On some difficulties with a posterior probability approximation technique, Bayesian Analysis, 3(2), 427-442

Consonni and Marin (2007) Mean field variational Bayesian inference for latent variable models, Computational Statistics and Data Analysis, 52(2), 790-798

Douc, Guillin, Marin and Robert (2007) Minimum variance importance sampling via Population Monte Carlo, ESAIM: Probability and Statistics, 11, 427-447

Douc, Guillin, Marin and Robert (2007) Convergence of adaptive mixtures of importance sampling schemes, Annals of Statistics, 35(1), 420-448

Druilhet and Marin (2007) Invariant HPD and MAP based on Jeffreys measure, Bayesian Analysis, 2(4), 681-692

Kendall, Marin and Robert (2007) Confidence bands for Brownian motion and applications to Monte Carlo simulations, Statistics and Computing, 17(1), 1-10

Marin (2007) Estimation of variance components for a linear Toeplitz model, Communications in Statistics: Theory and Methods, 36(12), 2273-2288

Celeux, Marin and Robert (2006) Iterated importance sampling in missing data problems, Computational Statistics and Data Analysis, 50(12), 3386-3404

Celeux, Marin and Robert (2006) Selection bayesienne de variables en regression lineaire, Journal de la Societe Française de Statistique, 147(1), 59-79

Guillin, Marin and Robert (2005) Estimation bayesienne approximative par echantillonnage preferentiel, Revue de Statistique Appliquee, LIII(1), 79-95

Cappe, Guillin, Marin and Robert (2004) Population Monte Carlo, Journal of Computational and Graphical Statistics, 13(4), 907-929

Marin and Dhorne (2003) Optimal quadratic unbiased estimation for models with linear Toeplitz covariance structure, Statistics, 37(2), 85-99

Marin and Dhorne (2002) Linear Toeplitz covariance structure models with optimal estimators of variance components, Linear Algebra and Its Applications, 354(1-3), 195-212

Book chapters

Celeux, Kamary, Malsiner-Walli, Marin, Robert (2019) Computational Solutions for Bayesian Inference in Mixture Models, In Handbook of Mixture Analysis, chapter 5, Chapman and Hall/CRC,

Estoup, Verdu, Marin, Robert, Dehne-Garcia, Cornuet and Pudlo (2019) Application of approximate Bayesian computation to infer the genetic history of Pygmy hunter-gatherers populations from Western Central Africa, In Handbook of Approximate Bayesian Computation, chapter 18, Chapman and Hall/CRC

Marin, Pudlo, Estoup and Robert (2019) Likelihood-free model choice, In Handbook of Approximate Bayesian Computation, chapter 6, Chapman and Hall/CRC

Robert, Marin and Rousseau (2011) Bayesian Inference and Computation, In Handbook of Statistical Systems Biology, chapter 3, John Wiley & Sons

Marin and Robert (2010) Importance sampling methods for Bayesian discrimination between embedded models, In Frontiers of Statistical Decision Making and Bayesian Analysis, pages 513-527, Springer-Verlag

Robert and Marin (2010) On computational tools for Bayesian analysis, In Rethinking Risk Measurement and Reporting, Volume I, Uncertainty, Bayesian Analysis and Expert Judgement, chapter 2, Risk Books

Lee, Marin, Mengersen and Robert (2009) Bayesian inference on mixtures of distributions, In Perspectives in Mathematical Sciences I, Probability and Statistics, pages 165–202, World Scientific

Marin, Mengersen and Robert (2005) Bayesian modelling and inference on mixtures of distributions, In Handbook of Statistics 25, Bayesian Thinking Modeling and Computation, pages 459-507, Elsevier


Marin, Pudlo and Sedki (2012) Optimal parallelization of a sequential approximate Bayesian computation algorithm, WSC 2012, Berlin

Baudry, Celeux and Marin (2008) Selecting models focussing on the modeller’s purpose, COMPSTAT 2008: Proceedings in Computational Statistics (P. Brito, Ed.), Physica-Verlag, Heidelberg, 337-348


Rousset and Marin (2018) Editorial for the Special Issue on Models and Inference in Population Genetics, Journal de la Société Française de Statistique, 159(3), 124-125


Marin, Josse and Robert (2017) Discussion on a paper of A. Gelman and C. Hennig: Beyond subjective and objective in statistics, Journal of the Royal Statistical Society Series A, 180, 4

Marin and Robert (2012) Discussion on a paper of P. Fearnhead and D. Prangle: Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation, Journal of the Royal Statistical Society Series B, 74, 3

Marin and Robert (2011) Discussion on a paper of M. Girolami and B. Calderhead: Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society Series B, 73, 2

Iaccobucci, Marin, Robert and Mengersen (2011) Discussion on a paper of H. Lopes, C. Carvalho, M. Johannes and N. Polson: Particle Learning for Sequential Bayesian Computation, Bayesian Statistics 9, Oxford University Press

Marin and Casarin and Robert (2009) Discussion on a paper of H. Rue, S. Martino and N. Chopin: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, Journal of the Royal Statistical Society Series B, 71, 2

Marin and Robert (2002) Discussion on a paper of S. L. Lauritzen and T. S. Richardson: Chain graph models and their causal interpretation, Journal of the Royal Statistical Society Series B, 64, 3


Marin and Robert (2009) Statistique bayesienne : les bases, Techniques de l'Ingenieur, AF605

François and Marin (2007) Initiation a R, La revue Modulad, 37, 83-101

Marin and Rossi (2004) Decouvrez les reseaux bayesiens, GNU/Linux Magazine France, 60, 56-65


PhD School: Decision, Informatique, Mathematiques et Organisation, University Paris Dauphine
Title: Adaptive Monte Carlo methods and Bayesian statistics
Coordinator: Christian P. Robert, Professor at University Paris Dauphine
Date: December 14th, 2007 at University Paris Dauphine
Committee members: Jean-Pierre Florens, Pascal Massart, Eric Moulines, Christian P. Robert and Judith Rousseau

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