Christian P. Robert

Selected publications/preprints

[1998|1999|2000|2001|2002|2003|2004|2005|2006|2007|2008|2009|2010|2011|2012|2013|2014|2015|2016|2017]|2018|2019|2020|2021|2022]

2022

Robert C.P. Measuring Abundance: Methods for the Estimation of Population Size and Species Richness by Graham Upton. CHANCE 35, 2, 55-56.
Robert, C.P. What Are the Chances? (Why We Believe in Luck) by Barbara Blatchley. CHANCE 35, 2, 56-57.
Robert, C.P. Learning Base R (2nd edition) by Lawrence Leemis. CHANCE 35, 2, 57-58.
Robert C.P. and Wu Changye Markov Chain Monte Carlo Methods, A Survey with Some Frequent Misunderstandings. In Computational Statistics in Data Science, Edited by W.W. Piegorsch, R.A. Levine, H.H. Zhang, and T.C.M. Lee, Wiley pp.119-150. Available as arXiv:2001.06249

2021

Carallo, G., Casarin, R. and Robert, C.P. A Bayesian Generalized Poisson Model for Cyber Risk Analysis Mathematical and Statistical Methods for Actuarial Sciences and Finance pp 123-128
Clarté, G., Robert, C.P., Ryder, R.J. and Stoehr, J, Componentwise approximate Bayesian computation via Gibbs-like steps, Biometrika, 108, 3, pp. 591–607
Casarin, R., Craiu, R., Frattarolo, L. and Robert, C.P. Living on the Edge: An Unified Approach to Antithetic Sampling. Available as arXiv:2110.15124
Martin, G., Frazier, D. and Robert, C.P. Computing Bayes: Bayesian Computation from 1763 to the 21st Century. Available as arXiv:2004.06425
Robert, C.P. Approximate Bayesian computation, an introduction. Statistics for Astrophysics, 77-112
Robert, C.P. and Wu, Changye Markov Chain Monte Carlo Methods, Survey with Some Frequent Misunderstandings. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels) Available as arXiv:2001.06249
Robert, C.P. and Roberts, G.O. Rao-Blackwellization in the MCMC era. Available as arXiv:2101.01011
Robert, C.P. Understanding Elections through Statistics, by Ole J. Forsberg. CHANCE 34.1, 52-53
Robert, C.P. Principles of Uncertainty, by J.B. Kadane CHANCE 34.1, 54-55
Robert, C.P. Statistics and Analysis of Scientific Data (Second Edition), by Massimiliano Boname CHANCE 34.1. 55-56.
Robert, C.P. The Error of Truth CHANCE, 34, 4, 34-36.
Robert, C.P. Poems that Solve Puzzles CHANCE, 34, 4, 36.
Robert, C.P. Quick (er) Calculations CHANCE, 34, 4, 34.
Thin, A., Janati, Y.,Le Corff, S., Ollion, Doucet, A., Dunmus, A., Moulines, E., and Robert, C.P. NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform. 2021. ⟨hal-03168489v2⟩
Thin, A., Janati, Y.,Le Corff, S., Ollion, Doucet, A., Dunmus, A., Moulines, E., and Robert, C.P. Invertible Flow Non Equilibrium Sampling. Avalaible as arXiv:2103.10943

2020

Carallo, G., Casarin, R., and Robert, C.P. Generalized Poisson difference autoregressive processes. Available as arXiv:2002.04470
Clarté. G. and Robert, C.P. A discussion of A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis, 15(2), 308-311.
Frazier D., Robert C., and Rousseau J. Model Misspecification in ABC: Consequences and Diagnostics, Journal of the Royal Statistical Society. Series B, Statistical Methodology 82(2), 421-444. Available as arXiv:1708.01974
Robert, C.P. The 9 Pitfalls of Data Science. CHANCE 33(1)
Robert, C.P. Prime Suspects. CHANCE 33(1)
Robert, C.P. and Wu C. Markov chain Monte Carlo methods, a survey with some frequent misunderstandings. Available as arXiv:2001.06249
Robert, C.P. Essentials of probability theory for statisticians CHANCE 33, 3, 61
Robert, C.P. A Computational Approach to Statistical Learning CHANCE 33, 3, 61-62
Vehtari, A., Gelman, A., Sivula, T., Jylänki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J.P., and Robert, C.P. Expectation propagation as a way of life. Journal of Machine Learning Research. 21(17), 1-53. Available as arxiv:1412.4869
Wu C. and Robert, C.P. Coordinate sampler: a non-reversible Gibbs-like MCMC sampler. Statistics and Computing, 30, 721-730.

2019

Banterle, M., Grazian, C., Lee, A., and Robert, C.P. Accelerating Metropolis-Hastings algorithms by delayed acceptance. Foundations of Data Science 1(2), 103-128. Available as arxiv:1503.00996
Bernton, E., Jacob, P., Gerber, M., and Robert, C.P. Inference in generative models using the Wasserstein distance. Journal of the Royal Statistical Society Series B 81(2), 235-269. Available as arXiv:1701.05146
Bernton, E., Jacob, P., Gerber, M., and Robert, C.P. On parameter estimation with the Wasserstein distance Information and Inference: A Journal of the IMA 8(4), 657-676.
Clarté, G., Robert, C.P., Ryder, R., and Stoehr, J. Component-wise approximate Bayesian computation via Gibbs-like steps. Available as 1905.13599
Früwirth-Schnatter, S., Celeux, G. and Robert. C.P. Handbook of Mixture Analysis. CRC Press, New York.
Gelman, A. et al. Many perspectives on Deborah Mayo’s “Statistical Inference as Severe Testing:How to Get Beyond the Statistics Wars”. pdf file.
Kamary, K., Lee, K., and Robert, C.P. Weakly non-informative priors for location-scale mixtures. J Computational and Graphical Statistics 27, 4, 836-848 Available as arXiv:1601.01178v3
Martin, G., McCabe, B.P.M., Frazier, D., Maneesoonthorn, W., and Robert, C.P. Auxiliary Likelihood-based Approximate Bayesian Computation in State Space Models, Journal of Computational and Graphical Statistics, 28, 508-522.
McShane, B.M., Gal, D., Gelman, A., Robert, C.P. and Tackett, J.L. Abandon Statistical Significance, The American Statistician, 73:sup1, 235-245
Raynal, L., Marin, J.-M., Pudlo, P., Ribatet, M., Robert, C.P., and Estoup, A. ABC random forests for Bayesian parameter inference, Bioinformatics, 35, n°10, p. 1720-1728
Robert, C.P. A discussion of "Latent Nested Nonparametric Priors" by Federico Camerlenghi, David B. Dunson, Antonio Lijoi, Igor Prünster, and Abel Rodrìguez. Bayesian Analysis 14(4), 1342-1343.
Robert, C.P. Practical Bayesian Inference: A primer for physical scientists International Statistical Review 87(1), 182-184
Robert, C.P. Practicals of Uncertainty CHANCE 32(1), 59
Robert, C.P. Ten Great Ideas about Chance CHANCE 32(1), 60-62
Robert, C.P. Independent Sampling Methods CHANCE 32(1), 62-63
Robert, C.P. Computational Methods for Numerical Analysis with R CHANCE 32(1), 63
Robert, C.P. The Beauty of Mathematics in Data Science CHANCE 32(2), 47
Robert, C.P. AIQ CHANCE 32(2), 48
Robert, C.P. Let the Evidence Speak. CHANCE 32(2), 49
Robert, C.P. Is this a big number? CHANCE 32(2), 50
Robert, C.P. Surprises in Probability - Seventeen short stories. CHANCE 32(2), 51
Wu. C. and Robert, C.P. Parallelising MCMC via Random Forests. Available as arXiv:1911.09698
Wu. C., Stoehr, J. and Robert, C.P. Faster Hamiltonian Monte Carlo by learning Leapfrog scale. Available as arXiv:1810.04449

2018

Drovandi, C.C., Grazian, C., Mengersen, K. and Robert, C.P. Approximating the likelihood in approximate Bayesian computation In: Sisson , Scott A., Fan , Yanan and Beaumont, Mark , (eds.) Handbook of Approximate Bayesian Computation. Boca Raton, Florida: CRC Press.
Frazier, D. T., Martin, G. M., Robert, C. P. and Rousseau, J. Asymptotic properties of approximate Bayesian computation. Biometrika, 105(3), 593-607.
Grazian, C. and Robert, C. P. Jeffreys priors for mixture estimation : properties and alternatives. Computational Statistics & Data Analysis, 121, 49-163.
Marin, J.-M., Pudlo, P., Estoup, A., and Robert, C.P. Likelihood-free model choice. In: Sisson , Scott A., Fan , Yanan and Beaumont, Mark (eds.) Handbook of Approximate Bayesian Computation. Boca Raton, Florida.
Robert, C. P. Beyond the Bayes factor, a new Bayesian paradigm for handling hypothesis testing. In: Chattopadhyay, Asis Kumar and Chattopadhyay, Gaurangadeb, (eds.) Statistics and its Applications. Singapore: Springer Nature Singapore Pte Ltd.. (In Press).
C. Robert Errors, Blunders, and Lies CHANCE 31 (1), 62-63
C. Robert The Seven Pillars of Statistical Wisdom CHANCE 31 (1), 61-62
C. Robert What Makes Variables Random: Probability for the Applied Researcher CHANCE 31 (1), 63-64
C. Robert Testing R Code CHANCE 31 (1), 60-60
Robert, C. P., Elvira, V., Tawn, N. and Wu, C. Accelerating MCMC algorithms. Wiley Interdisciplinary Reviews (WIRES): Computational Statistics (In Press)

2017

Frazier, D., Robert, C.P. and Rousseau, J. Model Misspecification in ABC: Consequences and Diagnostics. Available as arXiv:1708.01974
Jacob, P., Murray, L., Holmes, C., and Robert, C.P. Better together? Statistical learning in models made of modules. Available as arXiv:1708.08719
Kamary, K., Lee, K., and Robert, C.P. Weakly non-informative priors for location-scale mixtures. Available as arXiv:1601.01178v3
Marin, J.-M., Josse, J. and Robert, C.P. Some comments about "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig. Journal of the Royal Statistical Society Series A, 180(4), 48. Available as arXiv:1705.03727
Robert, C.P. Some comments about "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig. Journal of the Royal Statistical Society Series A, 180(4) 32-34. Available as arXiv:1705.03727
Robert, C.P. Une Vie Brève CHANCE 30(1), 40.
Robert, C.P. Statistical Rethinking CHANCE 30(1), 40-42.
Robert, C.P. Superintelligence: Paths, Dangers, Strategies CHANCE 30(1), 42-43.
Robert, C.P. and Rousseau, J. Some comments about A Bayesian criterion for singular models by M. Drton and M. Plummer. J. Royal Statistical Society, Series B, 79(2), 361-363. Available as arXiv:1610.02503.
Robert, C.P. and Rousseau, J. How Principled and Practical Are Penalised Complexity Priors? Some comments about "Penalising model component complexity" by Simpson et al. Statistical Science 31(1), 36-40.
Robert, C.P. and Rousseau, J. Nonparametric Bayesian Clay for Robust Decision Bricks. Some comments about "Penalising model component complexity" by Simpson et al. Statistical Science 31(1), 36-40. Available as arXiv:1609.06968
Wu, C. and Robert, C.P. Generalized Bouncy Particle Sampler. Available as arXiv:1706.04781
Wu, C. and Robert, C.P. Average of Recentered Parallel MCMC for Big Data. Available as arXiv:1706.04780

2016

Arbel, J. and Robert, C.P. Discussion on the paper of Varin, Cattelan and Firth ``Statistical modelling of citation exchange between statistics journals". J. Royal Statistical Society, Series A 179(1), 41-42.
Frazier, D.T., Martin, G.M., Rousseau, J. and Robert, C.P. Asymptotic Properties of Approximate Bayesian Computation. Available as arXiv:1508.05178
Lee, K., and Robert, C.P Importance Sampling Schemes for Evidence Approximation in Mixture Models Bayesian Analysis 11(2), 573-597. Available on Project Euclid.
Kamary, K., Lee, K., and Robert, C.P. Non-informative priors for location-scale mixtures. Available as arXiv:1601.01178
Marin, J.-M., Estoup, A. Pudlo, P., and Robert, C.P. Likelihood-free Model Choice. In Handbook of ABC by S. Sisson, L. Fan, and M. Beaumont. Available as arxiv:1503.07689
Marin, J.-M., Raynal, L. Pudlo, P., Ribatet, M. and Robert, C.P. ABC random forests for Bayesian parameter inference. Available as arxiv:1605.05537
Martin, G., MCCabe, B.P.M., Frazier, D., Maneesoonthorn, W., and Robert, C.P. Approximate Bayesian Computation in State Space Models. Available as arxiv:1604.07949
Mengersen, K.L., Drovandi, C.C., Pyne, D.B., Gore, C.J. and Robert, C.P. Bayesian estimation of small effects in exercise and sports science PLOS One 11(4): e0147311.
Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gauthier, M. and Robert, C.P. Reliable ABC model choice via random forests . Bioinformatics 32(6), 859-866. Available as arXiv:1406.6288
Robert, C.P. The expected demise of the Bayes factor. Journal of Mathematical Psychology 72, 33-37. Available as arXiv:1506.08292
Robert, C.P. Approximate Bayesian Computation: A Survey on Recent Methods. In Monte Carlo and Quasi-Monte Carlo Methods (MCqMC) 2014 (R. Cools and D. Nuyens, eds,). Springer Verlag, 195-205.
Robert, C.P. Monte Carlo Methods. Wiley StatsRef: Statistics Reference Online. 1–13.
Robert, C.P. Measuring Statistical Evidence Using Relative Belief CHANCE 29(3), 59-61.
Robert, C.P. Statistics Done Wrong: The Woefully Complete Guide CHANCE 29(3), 58.
Robert, C.P. Some comments about A. Ronald Gallant's "Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference". Journal of Financial Econometrics 14(2), 265--271. Available as arxiv:1502.01527
Robert, C.P. and Rousseau, J. Some comments about James Watson's and Chris Holmes' "Approximate Models and Robust Decisions": Nonparametric Bayesian clay for robust decision bricks Likelihood-free Model Choice. Statistical Science Volume 31, Number 4, 506-510. Available as arxiv:1603.090-88

2015

Banterle, M., Grazian, C., Lee, A., and Robert, C.P. Accelerating Metropolis-Hastings algorithms by delayed acceptance. Available as arxiv:1503.00996
Frazier, D.T., Martin, G.M. and Robert, C.P. On Consistency of Approximate Bayesian Computation. Available as arXiv:1508.05178
Grazian, C. and Robert, C.P. Jeffreys priors for mixture estimation. Available as arxiv:1511.03145
Grazian, C. and Robert, C.P. Jeffreys priors for mixture estimation. In Bayesian Statistics from Methods to Models and Applications: Research from BAYSM 2014 (S. Frühwirth-Schnatter, A. Bitto, G. Kastner, A. Posekany, Eds.). Springer-Verlag, Berlin.
Grazian, C., Masiani, I., and Robert, C.P. A discussion of "Bayesian model selection based on proper scoring rules" by A.P. Dawid and M. Musio. Bayesian Analysis 10(2), 511-515. Available as arxiv:1502.07638
Green, P.J., Łatuszyński, K., Pereyra, M., and Robert, C.P. Bayesian computation: a summary of the current state, and samples backwards and forwards. Statistics and Computing 25(4), 835-862. Available as arxiv:1502.01148
Moores, M.T., Drovandi, C., Mengersen, K.L., and Robert, C.P. Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing, 25(1), 23-33. Available as arxiv:1403.4359
Robert, C.P. The Metropolis-Hastings algorithm. Available as arxiv:1504.01896
Robert, C.P. Discussion on the paper of Gerber and Chopin "Quasi Monte Carlo". J. Royal Statistical Society, Series B 77(3), 569-570. Available as arxiv:1505.06473
Robert, C.P. Machine Learning, A Probabilist Perspective by K. Murphy CHANCE 27(2), 62-63.
Robert, C.P. and Marin, J.-M. Bayesian Essential with R: The complete solution manual. Available as arxiv:1503.04662
Salmeron, D., Caño, J.A., and Robert, C.P. Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions. Statistica Sinica 25(3), 1009-1023. Available as arxiv.org/abs/1306.6928.

2014

from the top of Ben Nevis, Scotland, Apr. 22, 2012

Banterle, M., Grazian, C. and Robert, C.P. Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching. Available as arxiv:1406.2660
Gelman, A. and Robert, C.P. Revised evidence for statistical standards. Proc. National Academy Sciences 111(19) E1935
Marin, J.-M., Pillai, N., Robert, C.P. and Rousseau, J. Relevant statistics for Bayesian model choice. J. Royal Statistical Soc. Series B 76, 833-859. Available as arxiv:1110.4700
Martin, G., MCCabe, B.P.M., Maneesoonthorn, W., and Robert, C.P. Approximate Bayesian Computation in State Space Models. Available as arxiv:1409.8363
Moreno, E., Vázquez–Polo, F.J., and Robert, C.P. Two discussions of the paper “Bayesian measures of model complexity and fit" by D. Spiegelhalter et al. J. Royal Statistical Soc. Series B 76(3), 486. Available as arxiv:1310.2905
Kamari, K. and Robert, C.P. Reflecting about Selecting Noninformative Priors. Available as arxiv:1402.6257
Kamari, K., Mengersen, K., Robert, C.P., and Rousseau, J. Testing as mixture estimtion. Available as arxiv:1412.2044
Robert, C.P. On the Jeffreys-Lindley paradox. Philosophy of Science 81, 216-232. Available as arxiv:1303.5973
Robert, C.P. Des spécificités de l'approche bayésienne et de ses justifications en statistique inférentielle. In Les approches et méthodes bayésiennes, sciences et épistémologie (ed. I. Drouet), Éditions Matériologiques (to appear). Available as arxiv:1403.4429
Robert, C.P. Discussion of "Deviance Information Criterion" by D. J. Spiegelhalter, N. G. Best, B. P. Carlin and A. van der Linde. J. Royal Statistical Society, Series B 76(3), 492.
Robert, C.P. Medical Illuminations: Using Evidence, Visualization and Statistical Thinking to Improve Healthcare by H. Wainer CHANCE 27(3), 57-58.
Robert, C.P. Statistical Modeling and Computation by D. Kroese and J. Chen CHANCE 27(2), 61-62.
Robert, C.P. Machine Learning, A Probabilist Perspective by K. Murphy CHANCE 27(2), 62-63.
Robert, C.P. Statistics for Spatio-Temporal Data by N. Cressie and C. Wikle CHANCE 27(2), 64.
Robert, C.P. The Cartoon Guide to Statistics by G. Klein and A. Dabney CHANCE 27(1), 61.
Robert, C.P. Naked Statistics by C. Wheelan CHANCE 27(1), 58-59.
Robert, C.P. The Most Human Human by B. Christian CHANCE 27(1), 57.
Robert, C.P. Bayesian Data Analysis by A. Gelman et al. J. American Statist. Assoc. 109(507), 1326-1327
Robert, C.P. Straightforward Statistics by G. Geher and S. Hall CHANCE 27(4), 58.
Robert, C.P. Foundations of Statistical Algorithms by C. Weihs, O. Mersmann and U. Ligges CHANCE 27(4), 59.
Robin, A.C., Reyle, C., Fliri, J., Czekaj, M., Robert, C.P. and Martins, A. M. M. Constraining the thick disc formation scenario of the Milky Way. Astronomy & Astrophysics 569, A13. Available as arXiv:1406.5384

2013

Atchadé, Y., Lartillot, N., and Robert, C.P. Bayesian computation for intractable normalizing constants. Brazilian Journal of Statistics 27(3), 417-436. Available as arXiv:0804.3152
Chopin, N., Gelman, A., Mengersen, K. and Robert, C.P. In praise of the referee. ISBA Bulletin 20(1), 13-18. Available as arXiv:1205.4304
Gelman, A. and Robert, C.P. “Not only defended but also applied”: The perceived absurdity of Bayesian inference (with discussion). The American Statistician 67(1), 1-5. Available as arXiv:1210.7225
Gelman, A., Robert, C.P., and Rousseau, J. Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin. Statistics & Risk Modeling 30, 1001-1016. Available as arxiv.org/pdf/1012.2184‎
Marin, J.-M. and Robert C.P. Bayesian Essentials with R. Springer-Verlag, New York.
Mengersen, K., Pudlo, P., and Robert, C.P. Bayesian computation via empirical likelihood. Proceedings of the National Academy of Sciences 110 (4), 1321–1326. Available as arXiv:1205.5658
Robert, C.P. Error and inference: an outsider stand on a frequentist philosophy. Theory and Decision 74, Issue 3, 447-461. Available as arXiv:1111.5827.
Robert, C.P. Bayesian Computational Tools. Annual Review of Statistics and Its Application / Volume 1, 153-177.
Robert, C.P. R for Dummies by A. de Vries and J. Meys CHANCE 26(4), 61.
Robert, C.P. Magical Mathematics: The Mathematical Ideas that Animate Great Magic Tricks by P. Diaconis and R. Graham CHANCE 26(2), 50-51.
Robert, C.P. Paradoxes in Statistical Inference by M. Chang CHANCE 26(2), 52-54.
Robert, C.P. In Pursuit of the Unknown: 17 Equations that Chnged the World by I. Stewart CHANCE 26(2), 54-58.
Robert, C.P. Guesstimation by L. Weinstein and J.A. Adam and Guesstimation 2.0 by L. Weinstein CHANCE 26(2), 58-59.

2012

Celeux, G., El Anbari, M., Marin, J.-M., and Robert, C.P. Regularization in Regression: Comparing Bayesian and Frequentist Methods in a Poorly Informative Situation. Bayesian Statistics 7(2), 477-502 (available on-line).
Cornuet, J.M., Marin, J.-M., Mira, A. and Robert, C.P. Adaptive Multiple Importance Sampling. Scandinavian Journal of Statistics 39(4), 798--812. Available as arXiv:0907.1254
Estoup, A., Lombaert, E., Marin, J.-M., Guillemaud, T., Pudlo, P., Robert, C.P., and Cornuet, J.-M. Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics. Molecular Ecology Ressources 12(5), 846--855. Available as pdf file.
Robert, C.P. The theory that would not die, by Sharon Bertsch McGrayne. CHANCE 25(1), 49-50. Available as pdf file.
Robert, C.P. The cult of significance, by Stephen Ziliak and Deirdre McCloskey. CHANCE 25(1). 51-53 Available as pdf file.
Robert, C.P. Handbook of Markov chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng. CHANCE 25(1). 53-55.Available as pdf file.
Robert, C.P. Handbook of fitting statistical distributions with R by Z. Karian and E.J. Dudewicz: CHANCE 25(1), 56-57. Available as pdf file.
Robert, C.P. Bayesian modeling using WinBUGS by Ioannis Ntzoufras. CHANCE 25(2).Available as pdf file.
Robert, C.P. Bayesian ideas and data analysis by Ronald Christensen, Wesley Johnson, Adam Branscum, and Timothy Hanson. CHANCE 25(2).Available as pdf file.
Robert, C.P. Understanding computational Bayesian statistics by William Boldstad. CHANCE 25(2). Available as pdf file.
Robert, C.P. Principles of Applied Statistics by David Cox and Christl Donnely. CHANCE 25(3), 58-59.
Robert, C.P. Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction by Brad Efron. CHANCE 25(3), 59-61.
Robert, C.P. A whistle-stop tour of Statistics by Brian Everitt. CHANCE 25(3), 61.
Robert, C.P. Correlations between the physical and social sciences by Valentine Belfiglio. CHANCE 25(3), 62.
Robert, C.P. Principles of Uncertainty by Joseph Kadane. JASA (to appear). Available as pdf file.

2011

Beffy, M. and Robert, C.P. Discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead. Journal of the Royal Statistical Society, Series B, 73(2), 173.
Douc, R. and Robert, C.P, A vanilla Rao--Blackwellisation of Metropolis-Hastings algorithms. Annals of Statistics 39(1), 261-277. Available as arXiv:0904.2144v2
Jacob, P., Robert, C.P., and Smith, M. Using parallel computation to improve Independent Metropolis--Hastings based estimation. Journal of Computational and Graphical Statistics 20(3): 616-635. Available as arXiv:1010.1595
Hobert, J.P., Roy, V. and Robert, C.P. Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modeling. Statistical Science 3(2011), 332-351. Available as pdf file.
Marin, J.-M., Pillai, N., Robert, C.P. and Rousseau, J. Relevant statistics for Bayesian model choice. Available as arXiv:1110.4700
Marin, J.-M., Pudlo, P., Robert, C.P., and Ryder, R. Approximate Bayesian Computational methods. Statistics and Computing 21(2), 289-291. Available as arXiv:1101.0955
Marin, J.-M. and Robert, C.P. Discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead. Journal of the Royal Statistical Society, Series B, 73(2), 189-190.
Robert, C.P. An attempt at reading Keynes' Treatise on Probability. International Statistical Review 79(1), 1-15. Available as arxiv:1003.4455
Robert, C.P. Evidence and Evolution: A review. Human Genomics 5(2), 130-136)). Available as arXiv:1004.5074
Robert, C.P. Computational Statistics: A review. Statistics and Computing (to appear). Available as pdf file
Robert, C.P. A Comparison of the Bayesian and frequentist approaches to estimation: A review. International Statistical Review 79(1), 117-118. Available as pdf file.
Robert, C.P. Bayesian model selection and statistical modeling: A review. International Statistical Review 79(1), 120-121. Available as pdf file.
Robert, C.P. Bayesian decision analysis: A review. International Statistical Review 79(2), 272–273. Available as pdf file.
Robert, C.P. Time Series: Modeling, Computation, and Inference: A review. International Statistical Review 79(2), 277–279. Available as pdf file.
Robert, C.P. A handbook of statistical analyses with R: A review. International Statistical Review 79(2), 276–277. Available as pdf file.
Robert, C.P. The foundations of Statistics: a simulation-based approach: A review. International Statistical Review (to appear). Available as pdf file.
Robert, C.P. The foundations of Statistics: a simulation-based approach by Shravan Vasishth and Michael Broe. CHANCE 24(4), 59-60. Available as pdf file.
Robert, C.P. Numerical Analysis for Statisticians: A review. International Statistical Review (to appear). Available as pdf file.
Robert, C.P. Numerical Analysis for Statisticians, by Kenneth Lange. CHANCE 24(4), 58-59. Available as pdf file.
Robert, C.P. Handbook of fitting statistical distributions with R: A review. International Statistical Review (to appear). Available as pdf file.
Robert, C.P. Anathem, by Neal Stephenson. CHANCE 24(4), 60-61. Available as pdf file.
Robert, C.P. Discussion of `Is Bayes Posterior just Quick and Dirty Confidence?' by D. Fraser. Statistical Science 3(2011), 317-318. Available as pdf file.
Robert, C.P. Interactive comment on “DREAM(D): an adaptive Markov chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, posterior parameter estimation problems” by J. A. Vrugt. Hydrol. Earth Syst. Sci. Discuss., 8, C1353–C1356.
Robert, C.P. Discussion of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead. Journal of the Royal Statistical Society, Series B, 73(2), 168-170.
Robert, C.P. and Casella, G. A History of Markov Chain Monte Carlo-Subjective Recollections from Incomplete Data. Statistical Science 26(1), 102-115. Available as arXiv0808.2902
Robert, C.P., Cornuet, J.-M., Marin, J.-M. and Pillai, N.S. Lack of confidence in approximate Bayesian computational (ABC) model choice. PNAS (Open Access). 108(37), 15112-15117. Available as arXiv:1102.4432
Robert, C.P., Marin, J.-M. and Pillai, N.S. Why approximate Bayesian computational (ABC) methods cannot handle model choice problems (earlier version of the above). Available as arXiv:1101.5091

2010

Barthelmé, S., Beffy, M. Chopin, N., Doucet, A., Jacob, P., Johansen, A.M., Marin, J.-M., and Robert, C.P. Discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead. Journal of the Royal Statistical Society, Series B (to appear). Available as arXiv:1011.0834
Beaumont, M.A., Nielsen, R., Robert, C.P., Hey, J., Gaggiotti, O., Knowles, L., Estoup, A., Mahesh, P., Coranders, J., Hickerson, M., Sisson, S., Fagundes, N., Chikhi, L., Beerli, P., Vitalis, R., Cornuet, J.-M., Huelsenbeck, J., Foll, M., Yang, Z., Rousset, F., Balding, D. and Excoffier, L. In defense of model-based inference in phylogeography. Molecular Ecology 19(3), 436-446.
Berger, J.O., Fienberg, S., Raftery. A. and Robert, C.P. Letter on Incoherent Phylogeographic Inference. Letter to PNAS, 107(41) E157. Available as arXiv:1006.3854
Casella, G. and Robert, C.P. Report of the Editors - 2009. J. Royal Statistical Society Series B, 72(1), 1-2.
Chopin, N., Iacobucci, A., Marin, J.-M., Mengersen, K.L., Robert, C.P., Ryder, R. and Schäfer, C. On particle learning (discussions on Lopes et al.). Bayesian Statistics 9 (to appear). Available as arXiv:1006.0554
Chopin, N. and Robert, C.P. Properties of Nested Sampling. Biometrika 97, 741-755 doi:10.1093/biomet/asq021. Available as arXiv:0801.3887
Chopin, N. and Robert, C.P. Discussion on Wilkinson's Parameter inference for stochastic kinetic models of bacterial gene regulation. Bayesian Statistics 9 (to appear). Available as pdf file
Hobert, J.O., Roy, V. and Robert, C.P. Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modelling. Available as arXiv:0911.4546
Iacobucci, A., Marin, J.-M., and Robert, C.P. On variance stabilisation by double Rao-Blackwellisation. Computational Statistics and Data Analysis 54, 698-710. Available as arXiv:0802.3690
Kilbinger, M., Wraith, D., Robert, C.P. , Benabed, K., Cappé, O., Cardoso, J.-F., Fort, G., Prunet, S., Bouchet, F. Bayesian model comparison in cosmology with population Monte Carlo. Monthly Notices of the Royal Astronomical Society: Letters. 405(4), 2381-2390 Available as arXiv:0912.1614.
Marin, J.-M., and Robert, C.P. On resolving the Savage-Dickey paradox. Electronic Journal of Statistics 4, 643-654. Available as arXiv:0910.1452
Marin, J.-M., and Robert, C.P. Importance sampling methods for Bayesian discrimination between embedded models. In Frontiers of Statistical Decision Making and Bayesian Analysis (eds., M.-H. Chen, D.K. Dey, P. Müller, D. Sun, K. Ye). Chapter 14, pages 513-553.
Robert, C.P. A Search for Certainty: A critical assessment. Bayesian Analysis (with discussion) 05, 02, 213-222. Available as arXiv:1001.5109
Robert, C.P. Bayesian computational methods.Handbook of Computational Statistics (Volume I) Concepts and Fundamentals, Chapter III.11. J. Gentle, W. Härdle, Y. Mori (eds) Springer-Verlag, Heidelberg (second edition). Available as arxiv:1002.2702
Robert, C.P. and Arbel, J. Discussion on Polson and Scott's Sparse Bayesian regularization and prediction. Available as pdf file
Robert, C.P. and Casella, G. Introducing Monte Carlo Methods with R: Solutions to Odd-Numbered Exercises. Available as arXiv:1001.2906
Robert, C.P. and Casella, G. A History of Markov Chain Monte Carlo-Subjective Recollections from Incomplete Data. In Handbook of Markov Chain Monte Carlo: Methods and Applications, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng (to appear). Available as arXiv0808.2902
Robert, C.P. and Casella, G. Generating Random Variables" (version 13). StatProb: The Encyclopedia Sponsored by Statistics and Probability Societies.
Robert, C.P. and Marin. J.-M. On computational tools for Bayesian analysis. In Rethinking Risk Measurement and Reporting, vol. 1, 29-68. Edited by K. Böcker. Available as arxiv:1002.2684
Robert, C.P. and Rousseau, J. On Bayesian data analysis. In Rethinking Risk Measurement and Reporting, vol. 1, 3-28. Edited by K. Böcker. Available as arxiv:1001.4656
Robert, C.P. and Rousseau, J. Discussion on Bernardo's Integrated objective Bayesian estimation and hypothesis testing. Bayesian Statistics 9 (to appear). Available as pdf file
Rousseau, J. and Robert, C.P. Discussion on Consonni and LaRocca's On moment priors for Bayesian model choice. Bayesian Statistics 9 (to appear). Available as pdf file

2009

Beaumont, M., Robert, C.P., Marin, J.-M. and Cornuet, J.M. Adaptivity for ABC algorithms: the ABC-PMC scheme. Biometrika 96(4), 983-990. Available as arXiv:08052256
Cucala, J., Marin, J.-M., Robert, C.P. and Titterington, D.M. A Bayesian reassessment of nearest--neighbour classification. Journal of the American Statistical Association, March 1, 2009, 104(485): 263-273. Available as doi:10.1198/jasa.2009.0125 | arXiv:0802.1357 | pdf file
Grelaud, A., Marin, J.-M., and Robert, C.P, ABC methods for model choice in Gibbs random fields, Notes aux Comptes Rendus de l'Académie des Sciences 347(3-4), 205-210.
Grelaud, A., Marin, J.-M., Robert, C.P., Rodolphe, F. and Tally, F. Likelihood-free methods for model choice in Gibbs random fields. Bayesian Analysis, 3(2), 427-442 . Revised version available as arXiv:0807.2767
Jacob, P., Chopin, N., Robert, C.P., and Rue, H. Comments on "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet and Hollenstein. Journal of the Royal Statistical Society (to appear). Available as arXiv:0911.0985
Lee, K., Mengersen, K.L., Marin, J.-M., and Robert, C.P. Bayesian Inference on Mixtures of Distributions. Perspectives in Mathematical Sciences. Stat. Sci. Interdiscip. Res., 7, 165-202. World Sci. Publ., Hackensack, NJ. Available as arXiv:0804.2413
Marin, J.-M and Robert, C.P., Les bases de la statistique bayésienne, Techniques de l'Ingénieur. AF 605. Earlier version available as pdf file
Robert, C.P Monte Carlo methods in Statistics. Available as arXiv:0909.0389
Robert, C.P On the relevance of the Bayesian approach to Statistics. Review of Economic Analysis (to appear). Available as arXiv:0909.5369
Robert, C.P Discussion of "Natural Induction: An objective Bayes approach" by Berger, Bernardo and Sun, Revista de la Real Academia of Ciencias, Series A Matemáticas) (to appear).
Robert, C.P. and Casella. G. Introducing Monte Carlo Methods with R. Use R! Springer Verlag, New York.
Robert, C.P., Chopin, N. and Rousseau, J. Harold Jeffreys' Theory of Probability revisited (with discussion). Statistical Science 24(2), 141-172 and 191-194 (reply to the discussion). Available as arXiv:0804.3173 and as arXiv:0909.1008 (reply to the discussion).
Robert, C.P. and Marin, J.-M. Bayesian Core: The Complete Solution Manual. Available as arXiv:0910.4696
Robert, C.P., Mengersen, K.L., and Chen, C. Model choice versus model criticism. Letter to PNAS (doi:10.1073/pnas.0911260107) 107(3), E5. Available as arXiv:0909.5673
Robert, C.P. and Wraith, D., Computational methods for Bayesian model choice. AIP Proceedings, Volume 1193, pp. 251-262 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; doi:10.1063/1.3275622. Available as arXiv:0907.5123.
Wraith, D., Kilbinger, M., Benabed, K., Cappé, O., Cardoso, J.-F., Fort, G., Prunet, S., Robert, C.P. Estimation of cosmological parameters using adaptive importance sampling. Physical Review D, 80, 023502. Available as arXiv:0903.0837

2008

Ben Mansour, S, Jouini, E., Marin, J.-M., Napp, C. and Robert, C.P. Are risk agents more optimistic? A Bayesian estimation approach. Journal of Applied Econometrics 23(6), 843-860.
Cano, J.A., Salmeron, D. and Robert, C.P. Integral equation solutions as prior distributions for Bayesian model selection. TEST 17(3), 493-504. Available as pdf file
Cappé, O., Douc, R., Gullin, A., Marin, J.-M. and Robert, C.P. Adaptive Importance Sampling in General Mixture Classes. Statistics and Computing 18, 447-459. Available as arXiv:0710.4242v1 | pdf file
Casarin, R. and Robert, C.P. Discussion of "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations” by Rue, Martino, and Chopin. Journal of the Royal Statistical Society pdf file.
Casella, G. and Robert, C.P. Report of the Editors — 2008. Journal of the Royal Statistical Society pdf file.
Chopin, N. and Robert, C.P. Contemplating Evidence: properties, extensions of, and alternatives to Nested Sampling. Programs available as progs.nc.tar.gz and progs.cpr.tar.gz. Revised version available as arXiv:0801.3887
Cornuet, J.M., Santos, F., Beaumont, M.A., Robert, C.P., Marin, J.-M., Balding, D.A., Guillemaud, T. and Estoup, A. Infering population history with DIY ABC: a user-friendly approach to Approximate Bayesian Computation. Bioinformatics 24(23), 2713-2719. Available as arXiv:0804.4372 | pdf file
Marin, J.-M, Casarin, R. and Robert, C.P., Discussion of "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations” by Rue, Martino, and Chopin. Journal of the Royal Statistical Society
Marin, J.-M and Robert, C.P., Approximating the marginal likelihood in mixture models. Bulletin of the Indian Chapter of ISBA V(1), 2-7. Available as arXiv0804.2414 | pdf file
Robert, C.P. Discussion of "Sure independence screening for ultra-high dimensional feature space" by Fan and Lv. Journal of the Royal Statistical Society 70(5), 901. pdf file.
Robert, C.P. Discussion of "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations” by Rue, Martino, and Chopin. Journal of the Royal Statistical Society pdf file.
Robert, C.P. À propos de l'article de N. Vayatis "Bayésiens contre fréquentistes, un faux débat". La Recherche 424, 6.
Robert, C.P. A message from the president. ISBA Bulletin 15(1), 15(2), 15(3), 15(4)
Robert, C.P. Misconceptions on Bayesianism. ISBA Bulletin 15(4), 2-3.
Robert, C.P. and Marin, J.-M., Some difficulties with some posterior probability approximations. Bayesian Analysis 3(2), 427-442. Available as arXiv:0801.3513

2007

Alston, C.L., Mengersen, K.L, Robert, C.P., Thompson, J.M., Littlefield, P.J. and Ball, A.J. Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images.Computational Statistics and Data Analysis, 51(9), 4282-4296.
Cappé, O. and Robert, C.P. Une approche Monte Carlo adaptative pour l’approximation de lois a posteriori avec application à l’inférence de paramètres cosmologiques. Proceedings, GRETSI, Troyes. Available as pdf file
Chopin, N. and Robert, C.P. Contemplating Evidence: properties, extensions of, and alternatives to Nested Sampling. Available as arXiv:0801.3887 | pdf file
Douc, R., Guillin, A., Marin, J.M., and Robert, C.P., Minimum variance importance sampling via population Monte Carlo. ESAIM Probability and Statistics 11, 427-447. Available as pdf
Douc, R., Guillin, A., Marin, J.-M. and Robert, C.P. Convergence of adaptive mixtures of importance sampling schemes, Annals of Statistics, 35(1), 420-448. Available as pdf|Snw
Kendall, W.S., Marin, J.-M. and Robert, C.P. Confidence bands for Brownian motion and applications to Monte Carlo simulations, Statistics and Computing , 17(1) 1-10. Available as pdf file
Marin, J.-M. and Robert, C.P., Bayesian Core: A Practical Approach to Computational Bayesian Statistics, Springer-Verlag, New York [webpage].
Robert, C.P. The Bayesian Choice. Paperback edition, Springer-Verlag.
Robert, C.P., Discussion of Jain and Neal's ``Splitting and merging components of a nonconjugate Dirichlet process mixture model". Bayesian Analysis. Available as pdf file

2006

Amzal, B., Bois, F.Y., Parent, E. and Robert, C.P. Bayesian optimal design via interacting MCM. J. American Statist. Assoc. 101, 773-785. Available as Postscript file.
Celeux, G., Marin, J.-M., and Robert, C.P., Sélection bayésienne de variables en régression linéaire. Journal de la Société Française de Statistique, 147, 1, 59-79. Available as pdf file
Celeux, G., Marin, J.M. and Robert, C.P. Iterated importance sampling in missing data problems. Computational Statistics and Data Analysis 50(12) 3386-3404. Available as PDF file.
Chopin, N. and Robert, C.P., A discussion of John Skilling's Nested sampling for the Valencia 8 Meeting. Available as pdf file. Reply from the author edited here
Müller, P., Robert, C.P. and Rousseau, J., Sample Size Choice for Microarray Experiments In Bayesian Inference for Gene Expression and Proteomics (eds. K.A. Do, P.Müller and M.Vannucci). Cambridge University Press.
Robert, C.P., Le Choix Bayésien : Principes et implémentation Springer-Verlag, Paris. [Springer order]
Robert, C.P., "A review of Gaussian Markov Random Fields (Theory and Applications) by Håvard Rue and Leonhard Held", Statistics in Medicine (to appear).
Robert, C.P., Three discussions on Bayesian model choice. Cahiers du Ceremade 2006-2. Available as pdf file

2005

Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M. Deviance information criteria for missing data models (with discussion) Bayesian Analysis 1(4), 651-674. Available as PDF file|R program|dataset1|dataset2
Guillin, A., Marin, J.M. and Robert, C.P. Estimation bayesienne approximative par echantillonnage preferentiel. Revue de Statistique Appliquée LIII, 1, 79-95 and Cahiers du Ceremade 0335. Available as PDF file.
Hobert, J.P., Jones, G.L. and Robert, C.P. Using a Markov chain to construct a tractable approximation of an untractable probability distribution. Scandinavian Journal of Statistics et Cahiers du Ceremade 0403. Available as PDF file.
Marin, J.M., Mengersen, K. and Robert, C.P. Bayesian modelling and inference on mixtures of distributions. Handbook of Statistics 25, D. Dey and C.R. Rao (eds). Elsevier-Sciences). Available as PDF file.

2004

Andrieu, C., Doucet, A. and Robert, C.P. Computational Advances for and from Bayesian Analysis. Statistical Science 19(1), 120-129. Available as PDF file.
Cappé, O., Guillin, A., Marin, J.M., and Robert, C.P., Population Monte Carlo. J. Comput. Graph. Stat. 13(4), 907-929 Available as Gzipped postscript.
Casella, G., Robert, C.P. and Wells, M.T., Mixture models, latent variables and partitioned importance sampling. Statist. Method. 1(1), 1-18.
Hobert, J.P. and Robert, C.P. A Mixture Representation of pi with Applications in Markov Chain Monte Carlo and Perfect Sampling. Annals of Applied Proba. 14(3), 1295-1305. Available as Compressed postscript.
Kendall, W.S., Marin, J.M., and Robert, C.P. Brownian confidence bands on Monte Carlo output. Cahiers du Ceremade. Available as PDF file.
Muller, P., Parmigiani, G., Robert, C.P. and Rousseau, J. Optimal Sample Size for Multiple Testing: the Case of Gene Expression Microarrays. J. American Stat. Assoc. 99, 990-1001. [Gzipped postscript|Slides]
Robert, C.P. Discussion on the Inverse Problem half-day. J. Royal Statis. Society. (to appear) [Slides|Written]
Robert, C.P. Bayesian computational methods. Handbook of Computational Statistics (Volume I) Concepts and Fundamentals, Chapter III.11. J. Gentle, W. Härdle, Y. Mori (eds) Springer-Verlag, Heidelberg . Available as PDF file.
Robert, C.P. and Casella, G. Monte Carlo Statistical Methods. Springer-Verlag, New York.

2003

Cappé, O., Robert, C.P., and Rydén, T. Reversible jump MCMC converging to birth-and-death MCMC and more general continuous time samplers. J. Royal Statis. Society Series B 65(3), 679-700. Available as Gzipped postscript.

Dupuis, J.A. and Robert, C.P. Bayesian variable selection in qualitative models by Kullback-Leibler projections. In J. Statistical Planning and Inference 111, 77-94. Available as Postscript.

Hurn, M., Justel, A. and Robert, C.P. Estimating mixtures of regressions. J. Comput. Graph. Stat. 12(1), 1-25. Available as [Compressed postscript | pdf].

Mengersen, K.L. and Robert, C.P. The pinball sampler. Bayesian Statistics 7 (edited by J.M. Bernardo, A.P. Dawid, J.O. Berger, and M. West) [Compressed postscript]

Philippe, A. and Robert, C.P. Perfect simulation of positive Gaussian distributions. Statistics and Computing 13(2), 179-186. [Compressed postscript]

Robert, C.P. Discussion of Kong, McCullagh, Nicolae, Tan, and Meng. J. Royal Statis. Society. 65(3), 606-609. [Slides |Written ]

Robert, C.P. Discussion of Brooks, Giudici and Roberts, J. Royal Statis. Society. 65(1), 39-42 Gzipped postscript.

Robert, C.P. and Rousseau, J. A mixture approach to Bayesian goodness of fit (revised version). Available as PDF file.

2002

Casella, G., Mengersen, K.L., Robert, C.P., and Titterington, D.M. Perfect Slice Samplers for Mixtures of Distributions. J. Royal Statis. Society Series B 64(4), 777-790. Available as Compressed postscript.
DeIorio, M. and Robert, C.P., Discussion of Spiegelhalter et al., J. Royal Statis. Society Series B 64(4), 629-630. Available as Gzipped postscript.
Douc, R., O. Cappé, E. Moulines, and C. P. Robert. On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models. Scandinavian J. Statist. 29(4), 615-636. [Abstract][Compressed postscript]
Doucet, A., Godsill, J.A. and Robert, C.P. Marginal maximum a posteriori estimation using Markov chain Monte Carlo. Statistics and Computing 12, 77-84 [Compressed postscript]
Marin, J.-M. and Robert, C.P. (2002) Discussion on a paper of S. L. Lauritzen and T. S. Richardson: Chain graph models and their causal interpretation,
J. Royal Statis. Society Series B, 64, 3
Robert, C.P. A review of Finite Mixture Models by G. McLachlan and D. Peel. J. American Statist. Assoc. (It actually never appeared!).
Robert, C.P. and Rousseau, J. A Mixture Approach to Bayesian Goodness of Fit. Cahier du CEREMADE 02009. Available as Gzipped postscript.
Robert, C.P. and Titterington, D.M. Discussion of Spiegelhalter et al., J. Royal Statis. Society Series B 64(4), 621-622. Available as Gzipped postscript.

2001

Altaleb, A. and Robert, C.P. Analyse bayesienne du modele Logit : algorithme par tranches ou Metropolis-Hastings ? Revue de Statistique Appliquée 49, 53-70.
Andrieu, Ch., and Robert, C.P. Controlled MCMC for Optimal Sampling. Available as Gzipped postscript.
Casella, G., Lavine, M. and Robert, C.P. Explaining the Perfect Sampler. The American Statistician 55(4), 299-305. Available as Compressed pdf file.
Philippe, A. and Robert, C.P. Riemann sums for MCMC estimation and convergence monitoring. Statistics and Computing 11, 103-115.
Robert, C.P. The Bayesian Choice. second edition, Springer-Verlag.

2000

Cappé, O. and Robert, C.P. Ten years and still running! J. American Statist. Assoc. 95 (4), 1282-1286. Available as html file.
Casella, G., Robert, C.P. and Wells, M.T. Rao-Blackwellization of Generalized Accept-Reject Schemes. Tech. Report, Dept. of Statistics, UFL. Available as Compressed postscript.
Casella, G., Robert, C.P. and Wells, M.T. Mixture models, latent variables and partitioned importance sampling. Tech. Report DT-2000-03, CREST, INSEE, Paris. Available as Compressed postscript.
Celeux, G., Hurn, M. and Robert, C.P. Computational and inferential difficulties with mixture posterior distributions. J. American Statist. Assoc. 95 957-970.
Doucet, A. and Robert, C.P. Maximum a posteriori parameter estimation for hidden Markov models. Tech. Report, Signal Processing Group, University of Cambridge. Available as Compressed postscript.
Fourdrinier, D., Philippe, A. and Robert, C.P. Estimation of a non-centrality parameter under Stein type like losses J. Statistical Planning and Inference 87(1), 43-54.
Robert, C.P., Rydén, T. and Titterington, D.M. Bayesian inference in hidden Markov models through jump Markov chain Monte Carlo J. Royal Statis. Society Series B 62(1), 57-75.

1999

Billio, M., Monfort, A. Robert, C.P. Bayesian estimation of switching ARMA models. J. Econometrics, 93 229-255. [Abstract][Full paper] (PDF).
Gruet, M.A., Philippe, A. and Robert, C.P. MCMC Control Spreadsheets for Exponential Mixture Estimation. J. Comput. Graph. Stat. 8, 298-317. See also the related software expmix.
Hobert, J. and Robert, C.P. Eaton's Markov chain, its conjugate partner and P-admissibility. Annals of Statistics 27, 361-373.
Hobert, J., Robert, C.P. and Titterington, D.M. On perfect simulation for some mixtures of distributions. Statistics and Computing 9 287-298.
Mengersen, K. L., Robert, C.P. and Guihenneuc-Jouyaux, C. MCMC convergence diagnostics: a "reviewww". In Bayesian Statistics 6 (J. Berger, J. Bernardo, A.P. Dawid and A.F.M. Smith,