Robert Christian P. - CV

Articles

Andral C., Douc R., Marival H., Robert C. (2024), The importance Markov Chain, Stochastic Processes and their Applications, vol. 171

Robert C., Rousseau J. (2023), A special issue on Bayesian Inference: Challenges, Perspective, and Prospects, Philosophical Transactions. Physical, Mathematical and Engineering Sciences, vol. 381, n°2247

Elvira V., Martino L., Robert C. (2022), Rethinking the Effective Sample Size, International Statistical Review, vol. 90, n°3, p. 525-550

Clarte G., Robert C., Ryder R., Stoehr J. (2021), Component-wise approximate Bayesian computation via Gibbs-like steps, Biometrika, vol. 108, n°3, p. 591–607

Robert C., Roberts G. (2021), Rao–Blackwellisation in the Markov Chain Monte Carlo Era, International Statistical Review, vol. 89, n°2, p. 237-249

Frazier D., Robert C., Rousseau J. (2020), Model Misspecification in ABC: Consequences and Diagnostics, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 82, n°2, p. 421-444

Wu C., Robert C. (2020), Coordinate sampler: a non-reversible Gibbs-like MCMC sampler, Statistics and Computing, vol. 30, p. 721–730

Raynal L., Marin J-M., Pudlo P., Ribatet M., Robert C., Estoup A. (2019), ABC random forests for Bayesian parameter inference, Bioinformatics, vol. 35, n°10, p. 1720-1728

Martin G., McCabe B., Frazier D., Maneesoonthorn W., Robert C. (2019), Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models, Journal of Computational and Graphical Statistics, vol. 28, n°3, p. 508-522

McShane B., Gal D., Gelman A., Robert C., Tackett J. (2019), Abandon Statistical Significance, The American Statistician, vol. 73, n°Sup.1, p. 235-245

Bernton E., Jacob P., Gerber M., Robert C. (2019), Approximate Bayesian computation with the Wasserstein distance, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 81, n°2, p. 235-269

Robert C., Elvira V., Tawn N., Wu C. (2018), Accelerating MCMC algorithms, Wiley Interdisciplinary Reviews. Computational Statistics, vol. 10, n°5, p. 1-22

Grazian C., Robert C. (2018), Jeffreys priors for mixture estimation: properties and alternatives, Computational Statistics & Data Analysis, vol. 121, p. 149-163

Kamary K., Lee J., Robert C. (2018), Weakly informative reparameterisations for location-scale mixtures, Journal of Computational and Graphical Statistics, vol. 27, n°4, p. 836-848

Frazier D., Martin G., Robert C., Rousseau J. (2018), Asymptotic Properties of Approximate Bayesian Computation, Biometrika, vol. 105, n°3, p. 593-607

Robert C., Rousseau J. (2017), How Principled and Practical Are Penalised Complexity Priors?, Statistical Science, vol. 32, n°1, p. 36-40

Robert C. (2016), Comment on: Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference, Journal of Financial Econometrics, vol. 14, n°2, p. 265-271

Pudlo P., Marin J-M., Estoup A., Cornuet J-M., Gautier M., Robert C. (2016), Reliable ABC model choice via random forests, Bioinformatics, vol. 32, n°6, p. 859-866

Mengersen K., Drovandi C., Robert C., Pyne D., Gore C. (2016), Bayesian Estimation of Small Effects in Exercise and Sports Science, PLoS ONE, vol. 11, n°4, p. e0147311

Robert C., Rousseau J. (2016), Nonparametric Bayesian Clay for Robust Decision Bricks, Statistical Science, vol. 31, n°4, p. 506-510

Robert C. (2016), The expected demise of the Bayes factor, Journal of Mathematical Psychology, vol. 72, p. 33-37

Green P., Latuszyski K., Pereyra M., Robert C. (2015), Bayesian computation: a perspective on the current state, and sampling backwards and forwards, Statistics and Computing, vol. 25, n°4, p. 835-862

Salmeron D., Cano J., Robert C. (2015), Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions, Statistica Sinica, vol. 25, n°3, p. 1009-1024

Robert C. (2015), Discussion on the paper "Sequential quasi Monte Carlo" by Gerber and Chopin, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 77, n°3, p. 2

Robert C., Grazian C., Masiani I. (2015), Comment on Article by Dawid and Musio, Bayesian Analysis, vol. 10, n°2, p. 511-515

Arbel J., Robert C., Prünster I., Ryder R. (2015), Three discussions of the paper "sequential quasi-Monte Carlo sampling", by M. Gerber and N. Chopin, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 77, n°3, p. 569-570

Moores M., Drovandi C., Mengersen K., Robert C. (2015), Pre-processing for approximate Bayesian computation in image analysis, Statistics and Computing, vol. 25, n°1, p. 23-33

Rousseau J., Pillai N., Marin J-M., Robert C. (2014), Relevant statistics for Bayesian model choice, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 76, n°5, p. 833-859

Kamary K., Robert C. (2014), Reflecting about Selecting Noninformative Priors, Journal of Applied and Computational Mathematics, vol. 3, n°5, p. n°1000175

Gelman A., Robert C. (2014), Revised evidence for statistical standards, PNAS - Proceedings of the National Academy of Sciences of the United States of America, vol. 111, n°19, p. E1936-E1937

Mengersen K., Robert C. (2014), Big Bayes Stories—Foreword, Statistical Science, vol. 29, n°1, p. 1

Reylé C., Robin A., Robert C., Fliri J., Czekaj M., Martins A. (2014), Constraining the thick disc formation scenario of the Milky Way, Astronomy & Astrophysics, vol. 569, p. n°A13

Robert C. (2014), On the Jeffreys-Lindley paradox, Philosophy of Science, vol. 81, n°2, p. 216-232

Robert C. (2014), Discussion of the paper "Bayesian measures of model complexity and fit" by D. Spiegelhalter et al., Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 76, n°3, p. 492-493

Robert C., Rousseau J., Gelman A. (2013), Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin, Statistics & Risk Modeling, vol. 30, n°2, p. 105-120

Robert C., Gelman A. (2013), "Not Only Defended But Also Applied" : The Perceived Absurdity of Bayesian Inference., The American Statistician, vol. 67, n°1, p. 1-5

Gelman A., Robert C., Mengersen K., Chopin N. (2013), In praise of the referee, ISBA Bulletin, vol. 20, n°1, p. 13-19

Robert C. (2013), Discussion, International Statistical Review, vol. 81, n°1, p. 52-56

Gelman A., Robert C. (2013), Rejoinder: The Anti-Bayesian Moment and Its Passing., The American Statistician, vol. 67, n°1, p. 16-17

Doucet A., Robert C. (2013), Introduction to Special Issue on Monte Carlo Methods in Statistics, ACM Transactions on Modeling and Computer Simulation, vol. 23, n°1, p. n°1

Pudlo P., Robert C., Mengersen K. (2013), Bayesian computation via empirical likelihood., PNAS - Proceedings of the National Academy of Sciences of the United States of America, vol. 110, n°4, p. 1321-1326

Robert C. (2013), Error and Inference: an outsider stand on a frequentist philosophy, Theory and Decision, vol. 74, n°3, p. 447-461

Robert C. (2013), The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy by Sharon Bertsch McGrayne, International Statistical Review, vol. 80, n°1, p. 178-179

Lartillot N., Robert C., Atchade Y. (2012), Bayesian computation for statistical models with intractable normalizing constants, Brazilian Journal of Probability and Statistics, vol. 27, n°4, p. 416-436

Marin J-M., Robert C., Celeux G., El Anbari M. (2012), Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation, Bayesian Analysis, vol. 7, n°2, p. 477-502

Marin J-M., Robert C., Ryder R., Pudlo P. (2012), Approximate Bayesian Computational methods, Statistics and Computing, vol. 22, n°6, p. 1167-1180

Cornuet J-M., Marin J-M., Mira A., Robert C. (2012), Adaptive Multiple Importance Sampling, Scandinavian Journal of Statistics, vol. 39, n°4, p. 798-812

Lombaert E., Cornuet J-M., Marin J-M., Robert C., Pudlo P., Estoup A. (2012), Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics., Molecular Ecology Resources, vol. 12, n°5, p. 846-855

Robert C. (2011), A Handbook of Statistical Analyses Using R, Second Edition by Brian S. Everitt, Torsten Hothorn: A review, International Statistical Review, vol. 79, n°2, p. 276-277

Robert C., Casella G. (2011), A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data, Statistical Science, vol. 26, n°1, p. 102-115

Robert C. (2011), Reading Keynes' Treatise on Probability, International Statistical Review, vol. 79, n°1, p. 1-15

Robert C. (2011), Book reviews, Fall 2011, Chance, vol. 24, n°4, p. 58-61

Robert C. (2011), Bayesian Decision Analysis: Principles and Practice by Jim Q. Smith: A review, International Statistical Review, vol. 79, n°2, p. 272-273

Robert C. (2011), Bayesian Model Selection and Statistical Modeling by Tomohiro Ando: A review, International Statistical Review, vol. 79, n°1, p. 120-121

Robert C. (2011), Time Series: Modeling, Computation, and Inference by Raquel Prado, Mike West: A review, International Statistical Review, vol. 79, n°2, p. 277-279

Robert C., Roy V., Hobert J. (2011), Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modelling, Statistical Science, vol. 26, n°3, p. 332-351

Jacob P., Robert C., Smith M. (2011), Using parallel computation to improve Independent Metropolis-Hastings based estimation, Journal of Computational and Graphical Statistics, vol. 20, n°3, p. 616-635

Robert C. (2011), A Comparison of the Bayesian and frequentist approaches to estimation by Francisco J. Samaniego: A review., International Statistical Review, vol. 79, n°1, p. 117-118

Robert C. (2011), James E. Gentle: Computational statistics, Statistics and Computing, vol. 21, n°2, p. 289-291

Robert C. (2011), Discussion on "Is Bayes posterior just quick and dirty confidence?" by D.A.S. Fraser, Statistical Science, vol. 26, n°3, p. 317-318

Douc R., Robert C. (2011), A vanilla Rao-Blackwellisation of Metropolis-Hastings algorithms, Annals of Statistics, vol. 39, n°1, p. 261-277

Marin J-M., Pillai N., Cornuet J-M., Robert C. (2011), Lack of confidence in approximate Bayesian computation model choice, PNAS - Proceedings of the National Academy of Sciences of the United States of America, vol. 108, n°37, p. 15112-15117

Robert C. (2010), The Search for Certainty: a critical assessment, Bayesian Analysis, vol. 5, n°2, p. 213-222

Fort G., Prunet S., Kilbinger M., Wraith D., Robert C., Benabed K. (2010), Bayesian model comparison in cosmology with Population Monte Carlo, Monthly Notices of the Royal Astronomical Society, vol. 405, n°4, p. 2381-2390

Chopin N., Robert C. (2010), Properties of Nested Sampling, Biometrika, vol. 97, n°3, p. 741-755

Beaumont M., Nielsen R., Robert C., Hey J., Gaggiotti O., Knowles L., Estoup A., Panchal M., Corander J., Hickerson M., Sisson S., Fagundes N., Chikhi L., Beerli P., Vitalis R., Cornuet J., Huelsenbeck J., Foll M., Yang Z., Rousset F., Balding D., Excoffier L. (2010), In defence of model-based inference in phylogeography, Molecular Ecology, vol. 19, n°3, p. 436-446

Robert C., Chen C., Mengersen K. (2010), Model choice versus model criticism, PNAS - Proceedings of the National Academy of Sciences of the United States of America, vol. 107, n°3

Robert C. (2010), On the relevance of the Bayesian approach to Statistics, Review of Economic Analysis, n°2, p. 139-152

Raftery A., Fienberg S., Berger J., Robert C. (2010), Incoherent phylogeographic inference, PNAS - Proceedings of the National Academy of Sciences of the United States of America, vol. 107, n°41, p. E157

Marin J-M., Robert C. (2010), On resolving the Savage–Dickey paradox, Electronic Journal of Statistics, vol. 4, p. 643-654

Iacobucci A., Robert C., Marin J-M. (2010), On variance stabilisation in population Monte Carlo by double Rao-Blackwellisation, Computational Statistics & Data Analysis, vol. 54, n°3, p. 698-710

Taly J-F., Grelaud A., Robert C., Marin J-M., Rodolphe F. (2009), ABC likelihood-free methods for model choice in Gibbs random fields, Bayesian Analysis, vol. 4, n°2, p. 317-336

Robert C., Rousseau J., Chopin N. (2009), Harold Jeffreys' Theory of Probability revisited, Statistical Science, vol. 24, n°2, p. 141-172

Robert C. (2009), Comments on: Natural Induction: An Objective Bayesian Approach, Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A, Matemáticas , vol. 103, n°1, p. 149-150

Kilbinger M., Cardoso J-F., Cappé O., Wraith D., Prunet S., Robert C. (2009), Estimation of cosmological parameters using adaptive importance sampling, Physical Review. D, Particles, Fields, Gravitation and Cosmology, vol. 80, n°2

Grelaud A., Marin J-M., Robert C. (2009), ABC methods for model choice in Gibbs random fields, Comptes rendus. Mathématique, vol. 347, n°3-4, p. 205-210

Robert C., Chopin N., Rousseau J. (2009), Rejoinder: Harold Jeffreys' Theory of Probability Revisited, Statistical Science, vol. 24, n°2, p. 191-194

Robert C., Titterington M., Cucala L., Marin J-M. (2009), A Bayesian reassessment of nearest-neighbour classification, Journal of the American Statistical Association, vol. 104, n°485, p. 263-273

Robert C., Marin J-M., Cornuet J-M., Beaumont M. (2009), Adaptive approximate Bayesian computation, Biometrika, vol. 96, n°4, p. 983-990

Robert C., Cano J., Salmeron D. (2008), Integral equation solutions as prior distributions for Bayesian model selection, Test, vol. 17, n°3, p. 493-504

Robert C. (2008), À propos de l'article de N. Vayatis" Bayésiens contre fréquentistes, un faux débat", La Recherche : L'actualité des sciences, vol. 424, p. 6

Robert C., Marin J-M. (2008), Some difficulties with some posterior probability approximations, Bayesian Analysis, vol. 3, n°2, p. 427-442

Robert C. (2008), Discussion of "Sure independence screening for ultra-high dimensional feature space" by Fan and Lv., Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 70, n°5, p. 901

Marin J-M., Robert C. (2008), On some difficulties with a posterior probability approximation technique, Bayesian Analysis, vol. 3, n°2, p. 427-442

Santos F., Beaumont M., Robert C., Cornuet J-M., Balding D., Marin J-M. (2008), Infering population history with DIY ABC : a user-friendly approach to Approximate Bayesian Computation, Bioinformatics, vol. 24, n°23, p. 2713-2719

Robert C., Wood A. (2008), Report of the Editors - 2007, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 70, n°1, p. 1-2

Jouini E., Ben Mansour S., Napp C., Marin J-M., Robert C. (2008), Are Risk-Averse Agents more Optimistic? A Bayesian Estimation Approach, Journal of Applied Econometrics, vol. 23, n°6, p. 843-860

Cappé O., Marin J-M., Douc R., Robert C., Guillin A. (2008), Adaptive Importance Sampling in General Mixture Classes, Statistics and Computing, vol. 18, n°4, p. 447-459

Robert C., Marin J-M. (2008), Approximating the marginal likelihood in mixture models, Indian Bayesian Society Newsletter, vol. V, n°1, p. 2-7

Kendall W., Robert C., Marin J-M. (2007), Confidence bands for Brownian motion and applications to Monte Carlo simulation, Statistics and Computing, vol. 17, n°1, p. 1-10

Perry D., Ball A., Mengersen K., Thompson J., Robert C., Alston C. (2007), Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images, Computational Statistics & Data Analysis, vol. 51, n°9, p. 4282–4296

Robert C., Marin J-M., Guillin A., Douc R. (2007), Convergence of adaptive mixtures of importance sampling schemes, Annals of Statistics, vol. 35, n°1, p. 420-448

Guillin A., Douc R., Robert C., Marin J-M. (2007), Minimum variance importance sampling via Population Monte Carlo, ESAIM. Probability and Statistics, vol. 11, p. 427-447

Robert C. (2007), Comment on Article by Jain and Neal, Bayesian Analysis, vol. 2, n°3, p. 483-494

Robert C., Titterington M., Celeux G., Forbes F. (2006), Deviance Information Criteria for Missing Data Models, Bayesian Analysis, vol. 1, n°4, p. 651-674

Robert C., Hobert J., Jones G. (2006), Using a Markov Chain to Construct a Tractable Approximation of an Intractable Probability Distribution, Scandinavian Journal of Statistics, vol. 33, n°1, p. 37-51

Marin J-M., Celeux G., Robert C. (2006), Sélection bayésienne de variables en régression linéaire, Journal de la Société française de statistique, vol. 147, n°1, p. 59-80

Amzal B., Robert C., Bois F., Parent E. (2006), Bayesian-Optimal Design via Interacting Particle Systems, Journal of the American Statistical Association, vol. 101, n°474, p. 773-785

Forbes F., Titterington M., Celeux G., Robert C. (2006), Rejoinder, Bayesian Analysis, vol. 1, n°4, p. 701-706

Robert C., Marin J-M., Celeux G. (2006), Iterated importance sampling in missing data problems, Computational Statistics & Data Analysis, vol. 50, n°12, p. 3386-3404

Marin J-M., Robert C., Guillin A. (2005), Estimation bayésienne approximative par échantillonnage préférentiel, Revue de statistique appliquée, vol. 53, n°1, p. 79-95

Hobert J., Robert C. (2004), A mixture representation of π with applications in Markov chain Monte Carlo and perfect sampling, Annals of Applied Probability, vol. 14, n°3, p. 1295-1305

Casella G., Wells M., Robert C. (2004), Mixture models, latent variables and partitioned importance sampling, Statistical Methodology, vol. 1, n°1-2, p. 1-18

Rousseau J., Parmigiani G., Robert C., Müller P. (2004), Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays, Journal of the American Statistical Association, vol. 99, n°468, p. 990-1001

Cappé O., Guillin A., Marin J-M., Robert C. (2004), Population Monte Carlo, Journal of Computational and Graphical Statistics, vol. 13, n°4, p. 907-929

Robert C., Cappé O., Ryden T. (2003), Reversible jump, birth-and-death and more general continuous time Markov chain Monte Carlo samplers, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 65, n°3, p. 679-700

Robert C., Justel A., Hurn M. (2003), Estimating Mixtures of Regressions, Journal of Computational and Graphical Statistics, vol. 12, n°1, p. 55-79

Robert C., Philippe A. (2003), Perfect simulation of positive Gaussian distributions, Statistics and Computing, vol. 13, n°2, p. 179-186

Titterington M., Robert C., Casella G., Mengersen K. (2002), Perfect samplers for mixtures of distributions, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 64, n°4, p. 777-790

Lavine M., Robert C., Casella G. (2001), Explaining the Perfect Sampler, The American Statistician, vol. 55, n°4, p. 299-305

Robert C. (1995), Simulation of truncated normal variables, Statistics and Computing, vol. 5, n°2, p. 121-125

Ouvrages

Robert C., Marin J-M. (2014), Bayesian Essentials with R (2nd ed.), Berlin: Springer, 296 p.

Robert C., Casella G. (2011), Méthodes de Monte-Carlo avec R, Berlin: Springer, 256 p.

Titterington M., Robert C., Mengersen K. (2011), Mixtures. Estimation and Applications, Chichester: Wiley, 308 p.

Robert C., Casella G. (2009), Introducing Monte Carlo Methods with R Springer, 284 p.

Robert C., Marin J-M. (2007), Bayesian Core: A practical approach to computational Bayesian statistics, New York: Springer, 258 p.

Robert C. (2007), The Bayesian Choice: From Decision Theoretic Foundations to Computational Implementation, New York: Springer, 577 p.

Robert C. (2006), Le Choix Bayésien : principes et pratique, Paris: Springer, 638 p.

Casella G., Robert C. (2004), Monte Carlo Statistical Methods, New York: Springer, 645 p.

Chapitres d'ouvrage

Robert C. (2022), 50 shades of Bayesian testing of hypotheses, in Arni S.R. Srinivasa Rao, G. Alastair Young, C.R. Rao, Handbook of Statistics- Volume 47 - Advancements in Bayesian Methods and Implementation, Amsterdam: Elsevier, p. 304

Robert C. (2021), Approximate Bayesian computation, an introduction, in Jean-Baptiste Marquette , Didier Fraix‐Burnet , Stéphane Girard and Julyan Arbel, Statistics for Astrophysics, Les Ulis: EDP Sciences, p. 77-112

Robert C., Carallo G., Casarin R. (2021), A Bayesian Generalized Poisson Model for Cyber Risk Analysis, in Marco Corazza, Manfred Gilli, Cira Perna, Claudio Pizzi, Marilena Sibillo, Mathematical and Statistical Methods for Actuarial Sciences and Finance, Berlin Heidelberg: Springer, p. 123-128

Wu C., Robert C. (2020), Markov Chain Monte Carlo Algorithms for Bayesian Computation, a Survey and Some Generalisation, in Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert, Case Studies in Applied Bayesian Data Science, Berlin Heidelberg: Springer, p. 89-119

Robert C., Celeux G., Kamary K., Malsiner-Walli G., Marin J-M. (2019), Computational Solutions for Bayesian Inference in Mixture Models, in Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert, Handbook of Mixture Analysis CRC Press, Taylor & Francis, p. 24

Marin J-M., Pudlo P., Estoup A., Robert C. (2018), Likelihood-free model choice, in Scott A. Sisson, Yanan Fan, Mark Beaumont, Handbook of Approximate Bayesian Computation CRC Press, Taylor & Francis, p. 662

Celeux G., Frühwirth-Schnatter S., Robert C. (2018), Model Selection for Mixture Models-Perspectives and Strategies, in Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert, Handbook of Mixture Analysis CRC Press, Taylor & Francis, p. 498

Mengersen K., Robert C. (2011), Exact Bayesian Analysis of Mixtures, in Titterington, Michael, Mixtures: Estimation and Applications, Chichester (UK): Wiley, p. 241-254

Marin J-M., Robert C., Rousseau J. (2011), Bayesian Inference and Computation, in Stumpf, Michael, Handbook of Statistical Systems Biology Wiley, p. 600

Robert C. (2011), Monte Carlo Methods in Statistics, in Lovric, Miodrag, International Encyclopedia of Statistical Science, Berlin: Springer, p. 854-858

Marin J-M., Robert C. (2010), Importance sampling methods for Bayesian discrimination between embedded models, in Chen M H., Müller P., Sun D., Ye K., Dey D., Frontiers of Statistical Decision Making and Bayesian Analysis, Berlin Heidelberg: Springer, p. 513-527

Marin J-M., Robert C. (2010), On computational tools for Bayesian data analysis, in Böcker, Klaus, Rethinking Risk Measurement and Reporting, London: Infopro Digital Risk Ltd, p. 527

Robert C. (2010), Bayesian computational methods (2e ed.), in Mori, Yuichi, Handbook of Computational Statistics, Berlin: Springer, p. 719-766

Robert C., Rousseau J. (2010), On Bayesian Data Analysis, in Böcker, Klaus, Rethinking Risk Measurement and Reporting, London: Infopro Digital Risk Ltd, p. 527

Mengersen K., Marin J-M., Robert C. (2005), Bayesian Modelling and Inference on Mixtures of Distributions, in Rao, C.R., Handbook of Statistics - Bayesian Thinking - Modeling and Computation Elsevier, p. 459-507

Casella G., Robert C., Wells M. (2004), Generalized Accept-Reject Sampling Schemes, in Rubin, Herman, A Festschrift for Herman Rubin, Beachwood (Ohio): Lecture Notes Monograph Series, p. 417

Directions d'ouvrage

Frühwirth-Schnatter S., Celeux G., Robert C. (2019), Handbook of Mixture Analysis CRC Press, Taylor & Francis, 522 p.

Communications avec actes

Robert C. (2016), Approximate Bayesian Computation: A Survey on Recent Results, in Ronald Cools, Dirk Nuyens, Monte Carlo and Quasi-Monte Carlo Methods. MCQMC, Leuven, Belgium, April 2014, Springer, 185-205 p.

Grazian C., Robert C. (2015), Jeffreys’ Priors for Mixture Estimation, in Sylvia Frühwirth-Schnatter, Angela Bitto, Gregor Kastner, Alexandra Posekany, Bayesian Statistics from Methods to Models and Applications / BAYSM 2014, Springer, 37-48 p.

Robert C. (2011), Simulation in Statistics, in Jain, S., Winter Simulation Conference, WSC 2011. Proceedings : 11-14 December 2011, Phoenix, Arizona, Phoenix, IEEE - Institute of Electrical and Electronics Engineers, 12 p.

Wraith D., Robert C. (2009), Computational methods for Bayesian model choice, in Goggans, Paul M., Bayesian Inference and Maximum Entropy Methods in Science and Engineering: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Oxford (Mississippi), American Institute of Physics, 12 p.

Communications sans actes

Robert C., Grazian C. (2014), Jeffreys Priors for Mixture Models, SIS 2014, Cagliari, Italie

Pudlo P., Leblois R., Robert C. (2012), Empirical likelihood for Bayesian inference in population genetics, Mathematical and Computational Evolutionnary Biology 2012, Montpellier, France

Féron O., Bouriga M., Robert C., Marin J-M. (2010), Bayesian Estimation of a Covariance Matrix: Application for Asset and Liabiliy Management, 9th Valencia International Meeting on Bayesian Statistics - 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Espagne

Rodolphe F., Grelaud A., Robert C. (2010), Détection de sélection darwinienne sur un gène par une approche sans vraisemblance, 42èmes Journées de Statistique, Marseille, France

Marin J-M., Robert C. (2010), On Resolving the Savage-Dickey Paradox, 9th Valencia International Meeting on Bayesian Statistics - 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Espagne

Rodolphe F., Marin J-M., Taly J-F., Grelaud A., Robert C. (2009), Choix de modèle pour les champs de Gibbs par un algorithme ABC., 41èmes Journées de Statistique, SFdS, Bordeaux, France

Jouini E., Napp C., Robert C., Ben Mansour S., Marin J-M. (2006), Are risk averse agents more optimistic ?, EGRIE, Barcelone, Espagne

Amzal B., Parent E., Bois F., Robert C. (2003), Optimisation de plans d'expérience par méthodes particulaires, 35e Journées de Statistiques, Lyon, France

Comptes-rendus d'ouvrages

Robert C. (2019), The Beauty of Mathematics in Computer Science, Chance, vol. 32, n°2, p. 47-48

Robert C. (2017), Une Vie Brève (One Hundred Twenty-One Days), Chance, vol. 30, n°1, p. 40-40

Robert C. (2017), Superintelligence: Paths, Dangers, Strategies, Chance, vol. 30, n°1, p. 42-43

Robert C. (2017), Statistical Rethinking, Chance, vol. 30, n°1, p. 40-42

Robert C. (2013), Book Reviews, Summer 2013, Chance, vol. 26, n°2

Robert C. (2012), Book reviews, Summer 2012, Chance, vol. 25, n°3

Donnet S., Robert C. (2012), Book reviews, Winter 2012, Chance, vol. 25, n°4

Alquier P., Robert C. (2012), Book reviews, Fall 2012, Chance, vol. 25, n°4, p. 57-61

Robert C. (2012), Book reviews, Summer 2012, Principles of Applied Statistics, Chance, vol. 25, n°3, p. 58-59

Iacobucci A., Robert C. (2012), Book Reviews (Spring 2012), Chance, vol. 25, n°2, p. 57-61

Robert C. (2012), Book reviews, Winter 2011, Chance, vol. 25, n°1, p. 49-57

Robert C. (2011), Bayesian Decision Analysis: Principles and Practice by Jim Q. Smith: A review, International Statistical Review, vol. 79, n°2, p. 272-273

Robert C. (2011), Bayesian Model Selection and Statistical Modeling by Tomohiro Ando: A review, International Statistical Review, vol. 79, n°1, p. 120-121

Robert C. (2011), Time Series: Modeling, Computation, and Inference by Raquel Prado, Mike West: A review, International Statistical Review, vol. 79, n°2, p. 277-279

Robert C. (2011), A Comparison of the Bayesian and frequentist approaches to estimation by Francisco J. Samaniego: A review., International Statistical Review, vol. 79, n°1, p. 117-118

Robert C. (2011), Book reviews, Fall 2011, Chance, vol. 24, n°4, p. 58-61

Robert C. (2011), A Handbook of Statistical Analyses Using R, Second Edition by Brian S. Everitt, Torsten Hothorn: A review, International Statistical Review, vol. 79, n°2, p. 276-277

Robert C. (2011), Evidence and Evolution: The logic behind the science, Human Genomics, vol. 5, p. 130-136

Prépublications / Cahiers de recherche

Stoehr J., Robert C. (2024), Simulating signed mixtures, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 30 p.

Luciano A., Robert C., Ryder R. (2023), Insufficient Gibbs Sampling, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 22 p.

McKimm H., Wang A., Pollock M., Robert C., Roberts G. (2022), Sampling using Adaptive Regenerative Processes, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 34 p.

Casarin R., Craiu R., Frattarolo L., Robert C. (2022), Living on the Edge: An Unified Approach to Antithetic Sampling, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 66 p.

Martin G., Frazier D., Robert C. (2022), Computing Bayes: From Then 'Til Now 1, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 21 p.

Hairault A., Robert C., Rousseau J. (2022), Evidence estimation in finite and infinite mixture models and applications, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 43 p.

Thin A., Janati Y., Le Corff S., Ollion C., Doucet A., Durmus A., Moulines É., Robert C. (2021), NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 36 p.

Clarte G., Ryder R., Robert C., Stoehr J. (2019), Component-wise approximate Bayesian computation via Gibbs-like steps, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 30 p.

Wu C., Stoehr J., Robert C. (2019), Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 18 p.

Wu C., Robert C. (2017), Generalized Bouncy Particle Sampler, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 28 p.

Bernton E., Jacob P., Gerber M., Robert C. (2017), Inference in generative models using the Wasserstein distance, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 34 p.

Kamary K., Mengersen K., Robert C., Rousseau J. (2017), Testing hypotheses via a mixture estimation model, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 37 p.

Wu C., Robert C. (2017), Average of Recentered Parallel MCMC for Big Data, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 14 p.

Celeux G., Jewson J., Josse J., Marin J-M., Robert C. (2017), Some discussions on the Read Paper Beyond subjective and objective in statistics" by A. Gelman and C. Hennig", Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 5 p.

Kamary K., Lee J., Robert C. (2016), Non-informative reparameterisations for location-scale mixtures, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL

Robert C., Rousseau J. (2016), Some comments about A Bayesian criterion for singular models by M. Drton and M. Plummer, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 4 p.

Banterle M., Grazian C., Robert C. (2014), Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 20 p.

Robert C. (2012), Principles of Uncertainty, by J.B. Kadane: A review, Paris, Université Paris-Dauphine, 4 p.

Marin J-M., Cornuet J-M., Sedki M., Pudlo P., Robert C. (2012), Efficient learning in ABC algorithms, Paris, Université Paris-Dauphine, 24 p.

Barthelme S., Beffy M., Chopin N., Doucet A., Jacob P., Johansen A., Marin J-M., Robert C. (2011), Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods", Paris, Université Paris-Dauphine, 6 p.

Robert C., Marin J-M., Pillai N., Rousseau J. (2011), Evaluating statistic appropriateness for Bayesian model choice, Paris, Université Paris-Dauphine, 18 p.

Marin J-M., Pillai N., Robert C. (2011), Why approximate Bayesian computational (ABC) methods cannot handle model choice problems, Paris, Université Paris-Dauphine, 20 p.

Robert C. (2011), First moments of the truncated and absolute Student's variates, Paris, Université Paris-Dauphine, 4 p.

Schäfer C., Mengersen K., Marin J-M., Robert C., Ryder R., Chopin N. (2010), On Particle Learning, Paris, Université Paris-Dauphine, 19 p.

Robert C. (2010), About incoherent inference, Paris, Université Paris-Dauphine, 3 p.

Jacob P., Chopin N., Robert C., Rue H. (2009), Comments on Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein", Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 9 p.

Robert C. (2006), Three discussions on model choice, Paris, Cahiers du CEREMADE, 17 p.

Guillin A., Cappé O., Marin J-M., Robert C. (2002), Population Monte Carlo for Ion Channel Restoration, Paris, Cahiers du CEREMADE, 15 p.

Robert C., Rousseau J. (2002), A Mixture Approach to Bayesian Goodness of Fit, Paris, Université Paris-Dauphine, 25 p.

Andrieu C., Robert C. (2001), Controlled MCMC for Optimal Sampling, Paris, Cahiers du CEREMADE, 38 p.

Casella G., Robert C., Wells M. (2001), Rao-blackwellization of generalized accept-reject schemes, Paris, Cahiers du CEREMADE, 10 p.

Hobert J., Robert C. (2001), Moralizing perfect sampling, Paris, Cahiers du CEREMADE, 22 p.

Doucet A., Robert C. (2000), Maximum a Posteriori Parameter Estimation for Hidden Markov Models, Paris, Cahiers du CEREMADE, 25 p.

Rapports

Robert C. (2013), Des spécificités de l'approche bayésienne et de ses justifications en statistique inférentielle, 18 p.

Editoriaux, directions de revue

Mira A., Robert C. (2015), An introduction to the special issue “Joint IMS-ISBA meeting - MCMSki 4”, Statistics and Computing, vol. 25, n° 1, p. 1