Christian P. Robert — Publications

2026

• Bandoni, E., Robert, C.P., and Stoehr, J. Optimal Sampling for Kernel Quadrature on Unbounded Domains. arXiv:2605.18134
• Bell, C., Johnston, T., Luciano, A., and Robert, C.P. Bayesian Adversarial Privacy. arXiv:2603.04199
• Bon, J.J., Bailie, J., Rousseau, J., and Robert, C.P. Persuasive Privacy. In Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea, PMLR 306. arXiv:2601.22945
• Naderi, D., Robert, C.P., Kamary, K., and Wraith, D. Approximating evidence via bounded harmonic means. Statistics and Computing, 36(3), 120. doi:10.1007/s11222-026-10875-z · arXiv:2510.20617
• Robert, C.P. and Raftery, A. Obituary: Paul Deheuvels 1948–2026. IMS Bulletin, 55(3), 15.
• Salmeron, D., Cano, J.A., and Robert, C.P. On integral priors for multiple comparison in Bayesian model selection. International Statistical Review (In press). doi:10.1111/insr.70028 · arXiv:2406.14184
• Sinha-Roy, S., Everitt, R.G., Robert, C.P., and Dutta, R. Prequential posteriors. Japanese Journal of Statistics and Data Science (In press). arXiv:2511.17721

2025

• Andral, C., Luciano, A., Robert, C.P., and Ryder, R.J. Permutations accelerate Approximate Bayesian Computation. arXiv:2507.06037
• McKimm, H., Pollock, M., Robert, C.P., Roberts, G.O., and Wang, A. Sampling using adaptive regenerative processes. Bernoulli, 31, 509–536. arXiv:2210.09901
• Robert, C.P. Hands-on Differential Privacy. CHANCE, book review, 38(1). doi:10.1080/09332480.2025.2473297
• Robert, C.P. Scalable Monte Carlo for Bayesian Learning. CHANCE, book review, 38(4). doi:10.1080/09332480.2025.2601495
• Robert, C.P. and Stoehr, J. Simulation of signed mixtures. Statistics and Computing, 35, 1–21. arXiv:2401.16828

2024

• Andral, C., Douc, R., Marival, H., and Robert, C.P. The importance Markov chain. Stochastic Processes and their Applications, 171, 104316. arXiv:2207.08271
• Bon, J. and Robert, C.P. Discussion of ``Safe testing" by Grünwald, de Heide, and Koolen. J. Royal Statistical Society Series B, 86, 1143–1145. arXiv:2402.14574
• Bon, J. and Robert, C.P. Discussion of ``Safe testing" by Grünwald, de Heide, and Koolen. J. Royal Statistical Society Series B, 86, 1156–1157.
• Carallo, G., Casarin, R., and Robert, C.P. Generalized Poisson difference autoregressive processes. International Journal of Forecasting, 40(4), 1359–1390. doi:10.1016/j.ijforecast.2023.11.009 · arXiv:2002.04470
• Casarin, R., Crau, R., Frattarolo, L., and Robert, C.P. Living on the Edge: An Unified Approach to Antithetic Sampling. Statistical Science, 39(1), 115–136.
• Frazier, D.T., Martin, G.M., and Robert, C.P. Computing Bayes: from Then 'til Now. Statistical Science, 39(1), 3–19. arXiv:2208.00646
• Frazier, D.T., Martin, G.M., and Robert, C.P. Approximating Bayes in the 21st Century. Statistical Science, 39(1), 20–45. arXiv:2112.10342
• Luciano, A., Robert, C.P., and Ryder, R.J. Insufficient Gibbs sampling. Statistics and Computing, 34, 1573–1375. arXiv:2307.14973
• Prangle, D. and Robert, C.P. Special Issue on the future of Bayesian Computing. Statistical Science.
• Prangle, D. and Robert, C.P. Bayesian Computations in the 21st Century. Statistical Science, 39(1), 1–2.
• Robert, C.P. Probably Overthinking It: How to Use Data to Answer Questions, Avoid Statistical Traps, and Make Better Decisions. CHANCE, book review, 37(3), 67–68. doi:10.1080/09332480.2024.2416877
• Robert, C.P. Mixture Models: Parametric, Semiparametric, and New Directions. CHANCE, book review, 37(4). doi:10.1080/09332480.2024.2434446
• Robert, C.P. Privacy-Preserving Computing for Big Data Analytics and AI. CHANCE, book review, 37(4). doi:10.1080/09332480.2024.2434447
• Robert, C.P. Objective Bayesian Inference. CHANCE, book review, 37(4). doi:10.1080/09332480.2024.2434448
• Robert, C.P. Philosophies, Puzzles and Paradoxes: A Statistician's Search for Truth. CHANCE, book review, 37(4). doi:10.1080/09332480.2024.2434449
• Robert, C.P. The Flawed Genius of William Playfair: The Story of the Father of Statistical Graphics. CHANCE, book review, 37(3). doi:10.1080/09332480.2024.2416876
• Robert, C.P. A Second Course in Probability. CHANCE, book review, 37(2). doi:10.1080/09332480.2024.2348976
• Robert, C.P. Exhalation. CHANCE, book review, 37(2). doi:10.1080/09332480.2024.2348977
• Sood, G., Gelman, A., and Robert, C.P. Noise: A Flaw in Human Judgment. CHANCE, book review, 37(3), 70–72. doi:10.1080/09332480.2024.2416879

2023

• Lawless, C., Robert, C.P., Rousseau, J., and Ryder, R.J. Asymptotics of approximate Bayesian computation when summary statistics converge at heterogeneous rates. arXiv:2311.10080
• Robert, C.P. Casanova's Lottery; Bayes Factors for Forensic Decision Analyses with R; Bayesian Probability for Babies. CHANCE, book review, 36(2), 33. doi:10.1080/09332480.2023.2203652
• Robert, C.P. Probabilistic Numerics. CHANCE, book review, 36(4). doi:10.1080/09332480.2023.2290960
• Robert, C.P. and Rousseau, J. A special issue on Bayesian inference: challenges, perspectives and prospects. Philosophical Transactions of the Royal Society A, 381(2247), 20220155 (Theme issue compiled and edited with M.I. Jordan). doi:10.1098/rsta.2022.0155

2022

• Benabed, K., Cappé, O., Cardoso, J.-F., Fort, G., Kilbinger, M., Prunet, S., Robert, C.P., and Wraith, D. pmclib: Population Monte Carlo library. Astrophysics Source Code Library (ascl:2211.008).
• Elvira, V., Martino, L., and Robert, C.P. Rethinking the Effective Sample Size. International Statistical Review, 90, 525–550.
• Hairault, A., Robert, C.P., and Rousseau, J. Evidence estimation in finite and infinite mixture models and applications. arXiv:2205.05416
• Robert, C.P. 50 shades of Bayesian testing of hypotheses. In Advancements in Bayesian Methods and Implementations, (eds. Young, A.G. and Srinivasa Rao, A.S.R. and Rao, C.R.), Chapter 10, 103–120. arXiv:2206.06659
• Robert, C.P. Bayes Rules!. CHANCE, book review, 35(4). doi:10.1080/09332480.2022.2145140
• Robert, C.P. Learning Base R (2nd edition) by Lawrence Leemis. CHANCE, book review, 35(2), 57–58. link
• Robert, C.P. Measuring Abundance: Methods for the Estimation of Population Size and Species Richness by Graham Upton.. CHANCE, book review, 35(2), 55–56. link
• Robert, C.P. What Are the Chances? (Why We Believe in Luck) by Barbara Blatchley. CHANCE, book review, 35(2), 56–57. link
• Robert, C.P. and Wu, C. Markov Chain Monte Carlo Methods, a survey with some frequent misunderstandings. In Computational Statistics in Data Science. arXiv:2001.06249 · link

2021

• Carallo, G., Casarin, R., and Robert, C.P. A Bayesian Generalized Poisson Model for Cyber Risk Analysis. In Mathematical and Statistical Methods for Actuarial Sciences and Finance, (eds. Corazza, M. and Gilli, M. and Perna, C. and Pizzi, C. and Sibillo, M), 123–128. Springer. link
• Casarin, R., Craiu, R., Frattarolo, L., and Robert, C.P. Living on the Edge: An Unified Approach to Antithetic Sampling. arXiv:2110.15124
• Clarté, G., Robert, C.P., Ryder, R.J., and Stoehr, J. Componentwise approximate Bayesian computation via Gibbs-like steps. Biometrika, 108(3), 591–607. doi:10.1093/biomet/asaa090 · arXiv:1905.13599
• Doucet, A., Durmus, A., Janati El Idrissi, Y., Le Corff, S., Moulines, E., Ollion, C., Robert, C.P., and Thin, A. NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. Advances in Neural Information Processing Systems, 34, 17060–17071. link
• Martin, G., Frazier, D., and Robert, C.P. Computing Bayes: Bayesian Computation from 1763 to the 21st Century. arXiv:2004.06425
• Robert, C.P. Poems that Solve Puzzles. CHANCE, book review. link
• Robert, C.P. Principles of Uncertainty, by J.B. Kadane. CHANCE, book review, 34(1), 54–55. link
• Robert, C.P. Quick (er) Calculations. CHANCE, book review. link
• Robert, C.P. and Roberts, G.O. Rao-–Blackwellisation in the Markov Chain Monte Carlo Era. International Statistical Review, 89, 237–249. arXiv:2101.01011
• Robert, C.P. Statistics and Analysis of Scientific Data (Second Edition), by Massimiliano Boname. CHANCE, book review, 34(1), 55–56. link
• Robert, C.P. The Error of Truth. CHANCE, book review. link
• Robert, C.P. Understanding Elections through Statistics, by Ole J. Forsberg. CHANCE, book review, 34(1), 52–53. link
• Thin, A., Janati, Y., Le Corff, S., Ollion, C., Doucet, A., Durmus, A., Moulines, E., and Robert, C.P. Invertible Flow Non Equilibrium Sampling. arXiv:2103.10943

2020

• Blomstedt, P., Cunningham, J.P., Gelman, A., Jylänki, P., Robert, C.P., Sahai, S., Schiminovich, D., Sivula, T., Tran, D., and Vehtari, A. Expectation propagation as a way of life. Journal of Machine Learning, 21(17), 1–53. arXiv:1412.4869 · link
• Clarté, G. and Robert, C.P. A discussion of ``A novel algorithmic approach to Bayesian logic regression" by A. Hubin, G. Storvik, and F. Frommlet. Bayesian Analysis, 15, 308–311. link
• Frazier, D.T., Robert, C.P., and Rousseau, J. Model Misspecification in ABC: Consequences and Diagnostics. Journal of the Royal Statistical Society. Series B, 82, 421–444. arXiv:1708.01974 · link
• Mengersen, K.L., Pudlo, P., and Robert, C.P. Case Studies in Applied Bayesian Data Science, CIRM Jean-Morlet Chair. Springer International Publishing, New York.
• Robert, C.P. A discussion of ``Unbiased Markov chain Monte Carlo with couplings" by P.E. Jacob, J. O'Leary and Y.F. Atchadé. J. Royal Statistical Society.
• Robert, C.P. A Computational Approach to Statistical Learning. CHANCE, book review, 33(3), 61–62. link
• Robert, C.P. Essentials of probability theory for statisticians. CHANCE, book review, 33(3), 61. link
• Robert, C.P. Prime Suspects. CHANCE, book review, 33(1). link
• Robert, C.P. The 9 Pitfalls of Data Science. CHANCE, book review, 33(1). link
• Robert, C.P. and Wu, C. Coordinate sampler: a non-reversible Gibbs-like MCMC sampler. Statistics and Computing, 30, 721–730. arXiv:1809.03388 · link

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. arXiv:1503.00996 · link
• Bernton, E., Gerber, M., Jacob, P.E., and Robert, C.P. On parameter estimation with the Wasserstein distance. Information and Inference, 8(4), 657–676. link
• Bernton, E., Gerber, M., Jacob, P.E., and Robert, C.P. Inference in generative models using the Wasserstein distance. J. Royal Statistical Society Series B, 81(2), 235–269. arXiv:1701.05146 · link
• Celeux, G., Frühwirth-Schnatter, S., and Robert, C.P. Handbook of Mixture Analysis. CRC Press, New York. link
• Frazier, D.T., Maneesoonthorn, W., Martin, G.M., McCabe, B.P.M., and Robert, C.P. Auxiliary likelihood-based approximate Bayesian computation in state space models. J. Computational and Graphical Statistics, 28, 1–34. arXiv:1604.07949 · link
• Gal, D., Gelman, A., McShane, B., Robert, C.P., and Tackett, J. Abandon statistical significance. The American Statistician, 73, 235–245. arXiv:1709.07588 · link
• Robert, C.P. AIQ. CHANCE, book review, 32(2), 48. link
• Robert, C.P. Computational Methods for Numerical Analysis with R. CHANCE, book review, 32(1), 63. link
• Robert, C.P. Independent Sampling Methods. CHANCE, book review, 32(1), 62–63. link
• Robert, C.P. Is this a big number?. CHANCE, book review, 32(2), 50. link
• Robert, C.P. Let the Evidence Speak. CHANCE, book review, 32(2), 49. link
• Robert, C.P. A discussion of ``Latent Nested Nonparametric Priors" by F. Camerlenghi, D.B. Dunson, A. Lijoi, I. Prünster, and A. Rodrìguez. Bayesian Analysis, 14(4), 1342–1343. link
• Robert, C.P. Practicals of Uncertainty. CHANCE, book review, 32(1), 59. link
• Robert, C.P. Surprises in Probability - Seventeen short stories. CHANCE, book review, 32(2), 51. link
• Robert, C.P. Ten Great Ideas about Chance. CHANCE, book review, 32(1), 60–62. link
• Robert, C.P. The Beauty of Mathematics in Data Science. CHANCE, book review, 32(2), 47. link
• Wu, C. and Robert, C.P. Parallelising MCMC via random forests. arXiv:1911.09698

2018

• Celeux, G., Frühwirth-Schnatter, S., and Robert, C.P. Model Selection for Mixture Models – Perspectives and Strategies. In Handbook of Mixture Analysis, Chapter 7, 117–154. arXiv:1812.09885
• Celeux, G., Kamary, K., Marin, J.-M., Robert, C.P., and Walli, G.M. Computational Solutions for Bayesian Inference in Mixture Models. In Handbook of Mixture Analysis, Chapter 5, 73–96. arXiv:1812.07240
• Cornuet, J.-M., Dehne-Garcia, C., Estoup, A., Marin, J.-M., Pudlo, P., Robert, C.P., and Verdu, M. Application of approximate Bayesian computation to infer the genetic history of Pygmy hunter-gatherers populations from Western Central Africa. In Handbook of ABC Methods, Chapter 18.
• Elvira, V., Robert, C.P., Tawn, N., and Wu, C. Accelerating MCMC algorithms. WIREs Computational Statistics, 10, e1435. arXiv:1804.02719 · link
• Frazier, D.T., Martin, G.M., Robert, C.P., and Rousseau, J. Asymptotic properties of Approximate Bayesian Computation. Biometrika, 105, 593–608. arXiv:1508.05178 · link
• Grazian, C. and Robert, C.P. Jeffreys priors for mixture estimation. Computational Statist. Data Analysis, 121, 149–163. link
• Kamary, K., Lee, A., and Robert, C.P. Weakly non-informative priors for location-scale mixtures. J. Computational & Graphical Statist, 27, 836–848. arXiv:1601.01178 · link
• Marin, J.-M., Pudlo, P., Raynal, L., Ribatet, V., and Robert, C.P. ABC random forests for Bayesian parameter inference. Bioinformatics, 35(10), 1720–1728. arXiv:1605.05537 · link
• Marin, J.-M., Pudlo, P., and Robert, C.P. Likelihood-free Model Choice. In Handbook of ABC Methods, Chapter 6, 1–21. arXiv:1503.07689
• Robert, C.P. Errors, Blunders, and Lies. CHANCE, book review, 31(1), 62–63. link
• Robert, C.P. Testing R Code. CHANCE, book review, 31(1), 60–60. link
• Robert, C.P. The Seven Pillars of Statistical Wisdom. CHANCE, book review, 31(1), 61–62. link
• Robert, C.P. What Makes Variables Random: Probability for the Applied Researcher. CHANCE, book review, 31(1), 63–64. link
• Wu, C., Pudlo, P., Robert, C.P., and Stoehr, J. Faster Hamiltonian Monte Carlo by learning leapfrog scale. arXiv:1810.04449

2017

• Jacob, P., Murray, L., Holmes, C., and Robert, C.P. Better together? Statistical learning in models made of modules. arXiv:1708.08719
• Josse, J., Marin, J.-M., and Robert, C.P. Some comments about "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig. J. Royal Statistical Society, 180. arXiv:1705.03727 · link
• Robert, C.P. Invited discussion on "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig. J. Royal Statistical Society, 180, 32–34.
• Robert, C.P. abcrf, with R codes for ABC model choice and parameter inference via random forests (contributions to the methodology and the associated paper, 2017).
• Robert, C.P. Ultimixt, with R codes for the Bayesian analysis of unidimensional mixture distributions via location-scale reparameterisation (contributions to the methodology and the associated paper, 2017).
• 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, 79(2), 361–363. arXiv:1610.02503
• Robert, C.P. and Rousseau, J. How principled and practical are penalised complexity priors?. Statistical Science, 32(1), 36–40. arXiv:1609.06968
• Robert, C.P. Statistical Rethinking. CHANCE, book review, 30(1), 40–42. link
• Robert, C.P. Superintelligence: Paths, Dangers, Strategies. CHANCE, book review, 30(1), 42–43. link
• Robert, C.P. Une Vie Brève. CHANCE, book review, 30(1), 40. link
• Wu, C. and Robert, C.P. Average of recentered parallel MCMC for big data. arXiv:1706.04780
• Wu, C. and Robert, C.P. Generalized bouncy particle sampler. arXiv:1706.04781

2016

• Cornuet, J.-M., J.M, E., Marin, J.-M., Pudlo, P., and Robert, C.P. Reliable ABC model choice via random forest. Bioinformatics, 32(6), 859–866. arXiv:1406.6288
• Drovandi, C.C., Gore, C.J., Mengersen, K.L., Pyne, D.B., and Robert, C.P. Bayesian estimation of small effects in exercise and sports science. PLOS One, 11(4), e0147311.
• Frazier, D.T., Martin, G.M., Robert, C.P., and Rousseau, J. Asymptotic Properties of Approximate Bayesian Computation. Related to frazier:martin:2018 (On Consistency of Approximate Bayesian Computation); possibly a revised/retitled version of the same work. arXiv:1607.06903
• Lee, A. and Robert, C.P. Importance Sampling Schemes for Evidence Approximation in Mixture Models. Bayesian Analysis, 11, 573–597. arXiv:1311.6000 · link
• Martin, G.M., McCabe, B.P.M., Maneesoonthorn, W., and Robert, C.P. Approximate Bayesian Computation in State Space Models. Earlier preprint, superseded by the published version (frazier:maneesoonthorn:2019). arXiv:1409.8363
• Robert, C.P. The expected demise of the Bayes factor. J. Mathematical Psychology, 72, 33–37. arXiv:1506.08292 · link
• Robert, C.P. Des spécificités de l'approche bayésienne et de ses justifications en statistique inférentielle. In Les Approches et Mééthodes bayésiennes. arXiv:1403.4429
• Robert, C.P. Approximate Bayesian Computation, a survey on recent results. In Monte Carlo and Quasi-Monte Carlo Methods 2014. link
• 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. arXiv:1502.01527
• Robert, C.P. Measuring Statistical Evidence Using Relative Belief. CHANCE, book review, 29(3), 59–61. link
• Robert, C.P. and Rousseau, J. Nonparametric Bayesian clay for robust decision bricks. Statistical Science, 31(4), 506–510. arXiv:1603.09088
• Robert, C.P. Statistics Done Wrong: The Woefully Complete Guide. CHANCE, book review, 29(3), 58. link

2015

• 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, 179, 41–42.
• Cano, J.A., Robert, C.P., and Salmeron, D. Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions. Statistica Sinica, 25, 1009–1023. arXiv:1306.6928
• Gelman, A. and Robert, C.P. Letter on Revised evidence for statistical standards. Proc. National Academy Sciences, 111, E1935. link
• 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, 511–515. arXiv:1502.07638 · link
• Grazian, C. and Robert, C.P. Jeffreys’ Priors for Mixture Estimation. In Bayesian Statistics from Methods to Models and Applications: Research from BAYSM 2014. arXiv:1511.03145
• Green, P.J., Pereyra, M., Robert, C.P., and Łatuszynski, K. Bayesian computation: a perspective on the current state, and sampling backwards and forwards. Statistics and Computing, 25, 835–862. arXiv:1502.01148
• Robert, C.P. Discussion on the paper of Gerber and Chopin ``Quasi Monte Carlo". J. Royal Statistical Society, 77, 569–570. arXiv:1505.06473
• Robert, C.P. The Metropolis-Hastings algorithm. Wiley StatsRef: Statistics Reference Online. arXiv:1504.01896 · link
• Robert, C.P. Machine Learning, A Probabilist Perspective by K. Murphy. CHANCE, book review, 27(2), 62–63.

2014

• Banterle, M., Grazian, C., and Robert, C.P. Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching. Earlier, superseded preprint; see banterle:grazian:2019 for the published version. arXiv:1406.2660
• Czekaj, M., Fliri, J., Martins, A.M.M., Reyle, C., Robert, C.P., and Robin, A.C. Constraining the thick disc formation scenario of the Milky Way. Astronomy & Astrophysics, 569, A13. arXiv:1406.5384
• Drovandi, C.C., Mengersen, K.L., Moores, M.T., and Robert, C.P. Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing, 25, 23–33. arXiv:1403.4359
• Kamary, K., Mengersen, K., Robert, C.P., and Rousseau, J. Testing hypotheses via a mixture estimation model. arXiv:1412.2044
• Kamary, K. and Robert, C.P. Reflecting about selecting noninformative priors. J. Appl. Computat. Math, 3, 175–182. arXiv:1402.6257
• Marin, J.-M., Pillai, N.S., Robert, C.P., and Rousseau, J. Relevant statistics for Bayesian model choice. Journal of the Royal Statistical Society, 76, 833–859. arXiv:1110.4700
• Mengersen, K.L. and Robert, C.P. Special Issue on Big Bayes Stories. Statistical Science.
• Moreno, E., Vazquez-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 Society, Series B, 76(3), 486. arXiv:1310.2905
• Robert, C.P. Special Issue on Markov Chain Monte Carlo. Statistics and Computing.
• Robert, C.P. On the Jeffreys-Lindley paradox. Philosophy of Science, 81, 216–232. arXiv:1303.5973
• 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.
• Robert, C.P. Foundations of Statistical Algorithms by C. Weihs, O. Mersmann and U. Ligges. CHANCE, book review, 27(4), 59.
• Robert, C.P. Machine Learning, A Probabilist Perspective by K. Murphy. CHANCE, book review, 27(2), 62–63.
• Robert, C.P. Medical Illuminations: Using Evidence, Visualization and Statistical Thinking to Improve Healthcare by H. Wainer. CHANCE, book review, 27(3), 57–58.
• Robert, C.P. Naked Statistics by C. Wheelan. CHANCE, book review, 27(1), 58–59.
• Robert, C.P. Statistical Modeling and Computation by D. Kroese and J. Chen. CHANCE, book review, 27(2), 61–62.
• Robert, C.P. Statistics for Spatio-Temporal Data by N. Cressie and C. Wikle. CHANCE, book review, 27(2), 64.
• Robert, C.P. Straightforward Statistics by G. Geher and S. Hall. CHANCE, book review, 27(4), 58.
• Robert, C.P. The Cartoon Guide to Statistics by G. Klein and A. Dabney. CHANCE, book review, 27(1), 61.
• Robert, C.P. The Most Human Human by B. Christian. CHANCE, book review, 27(1), 57.

2013

• Atchadé, Y., Lartillot, N., and Robert, C.P. Bayesian computation for intractable normalizing constants. Brazilian Journal of Statistics, 27, 417–436. arXiv:0804.3152
• Chopin, N., Gelman, A., Mengersen, K., and Robert, C.P. In praise of the referee. ISBA Bulletin, 20(1), 13–18. arXiv:1205.4304 · link
• 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 and Risk Modelling, 30, 1001–1016. arXiv:1012.2184
• Gelman, A. and Robert, C.P. ``Not only defended but also applied": The perceived absurdity of Bayesian inference (with discussion and rejoinder). The American Statistician, 67(1), 1–5. arXiv:1006.5366 · link
• Gelman, A. and Robert, C.P. The anti-Bayesian moment and its passing. The American Statistician (Reply to discussion). arXiv:1210.7225
• George, E. and Robert, C.P. Obituary of George Casella. Amstat News, march 2013, 6–7.
• Marin, J.-M. and Robert, C.P. Bayesian Essentials with R. Springer-Verlag, New York.
• Mengersen, K.L., Pudlo, P., and Robert, C.P. Bayesian computation via empirical likelihood. Proceedings of the National Academy of Sciences, 110, 1321–1326. arXiv:1205.5658 · link
• Robert, C.P. Special Issue on Advances in Markov Chain Monte Carlo. J. Computational and Graphical Statistics.
• Robert, C.P. Error and Inference: an outsider stand on a frequentist philosophy. Theory and Decision, 73(1), 1–15. arXiv:1111.5827
• Robert, C.P. Bayesian Computational Tools. In Annual Review of Statistics and Its Application, 153–177. arXiv:1304.2048 · link
• Robert, C.P. Discussion of ``Confidence distribution, the frequentist distribution estimator of a parameter: a review'' by Xie and Singh. International Statistical Review, 81, 51–56. arXiv:1206.1708
• Robert, C.P. Discussion of ``Bayesian Nonparametric Inference - Why and How", by Müller and Mitra. Bayesian Analysis, 8, 350–351.
• Robert, C.P. Principles of Uncertainty: a review. J. American Statist. Assoc. link
• Robert, C.P. Evidence and Evolution: a review. J. American Statist. Assoc.
• Robert, C.P. Remembering George Casella through his books. CHANCE, book review, 26.
• Robert, C.P. bayess, with R programs used in the books ``Bayesian Essentials with R" and ``Bayesian Core" (co-author with J.M. Marin, 2013).
• Robert, C.P. ARAMIS, with R codes for the Adaptive Multiple Importance Sampler (AMIS) algorithm (contributions to the methodology and the associated paper, 2013).
• Robert, C.P. Guesstimation by L. Weinstein and J.A. Adam and Guesstimation 2.0 by L. Weinstein. CHANCE, book review, 26(2), 58–59.
• Robert, C.P. In Pursuit of the Unknown: 17 Equations that Changed the World by I. Stewart. CHANCE, book review, 26(2), 54–58.
• Robert, C.P. Magical Mathematics: The Mathematical Ideas that Animate Great Magic Tricks by P. Diaconis and R. Graham. CHANCE, book review, 26(2), 50–51.
• Robert, C.P. Paradoxes in Statistical Inference by M. Chang. CHANCE, book review, 26(2), 52–54.
• Robert, C.P. R for Dummies by A. de Vries and J. Meys. CHANCE, book review, 26(4), 61.

2012

• Behesta, S. and Robert, C.P. Interview of Persi Diaconis on ``Magical Mathematics". CHANCE, book review, 25, 21–25.
• 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, 07, 477–502. arXiv:1010.0300 · link
• Chopin, N. and Robert, C.P. Discussion of ``Catching faster by switching sooner" by van Erven, Grünwald and de Rooij. Journal of the Royal Statistical Society, Series B, 74, 403–404.
• Cornuet, J.-M., Estoup, A., Guillemaud, T., Lombaert, E., Marin, J.-M., Pudlo, P., and Robert, C.P. Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics. Molecular Ecology Ressources, 12, 846–855. link
• Cornuet, J.-M., Marin, J.-M., Mira, A., and Robert, C.P. Adaptive Multiple Importance Sampling. Scandinavian J. Statist, 39, 798–812. arXiv:0907.1254
• Hobert, J.O., Robert, C.P., and Roy, M. Improving the convergence properties of the data augmentation algorithm with an application to Bayesian mixture modeling. Statistical Science, 3, 332–351. arXiv:0911.4546
• Robert, C.P. Special Issue on Monte Carlo methods in Statistics. ACM Transactions on Computer Machinery.
• Robert, C.P. Bayesian Computational Methods. In Handbook of Computational Statistics, 751–806. arXiv:1002.2702
• Robert, C.P. Discussion of ``Semi-automatic ABC" by Fearnhead and Prangle. Journal of the Royal Statistical Society, Series B, 74, 463–466. arXiv:1201.1314
• Robert, C.P. Interview of Sharon McGrayne on ``The Theory That Would Not Die". CHANCE, book review, 25, 24–29.
• Robert, C.P. The Theory That Would Not Die: a review. International Statistical Review, 80, 178–179. link
• Robert, C.P. Handbook of fitting statistical distributions with R: a review. International Statistical Review, 80, 177–178. link
• Robert, C.P. First moments of the truncated and absolute Student's variates. arXiv:1111.6110
• Robert, C.P. Reading Th\'eorie Analytique des Probabilit\'es. arXiv:1203.6249
• Robert, C.P. A whistle-stop tour of Statistics by Brian Everitt. CHANCE, book review, 25(3), 61.
• Robert, C.P. Bayesian ideas and data analysis by Ronald Christensen, Wesley Johnson, Adam Branscum, and Timothy Hanson. CHANCE, book review, 25(2).
• Robert, C.P. Bayesian modeling using WinBUGS by Ioannis Ntzoufras. CHANCE, book review, 25(2). link
• Robert, C.P. Correlations between the physical and social sciences by Valentine Belfiglio. CHANCE, book review, 25(3), 62.
• Robert, C.P. Handbook of fitting statistical distributions with R by Z. Karian and E.J. Dudewicz. CHANCE, book review, 25(1), 56–57.
• Robert, C.P. Handbook of Markov chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng. CHANCE, book review, 25(1), 53–55. link
• Robert, C.P. Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction by Brad Efron. CHANCE, book review, 25(3), 59–61.
• Robert, C.P. Principles of Applied Statistics by David Cox and Christl Donnely. CHANCE, book review, 25(3), 58–59.
• Robert, C.P. The cult of significance, by Stephen Ziliak and Deirdre McCloskey. CHANCE, book review, 25(1), 51–53. link
• Robert, C.P. Understanding computational Bayesian statistics by William Boldstad. CHANCE, book review, 25(2). link

2011

• Beffy, S., 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, 173–175. arXiv:1011.0834
• Casella, G. and Robert, C.P. Méthodes de Monte-Carlo avec R.
• Casella, G. and Robert, C.P. A short history of Markov Chain Monte Carlo. Statist. Science, 26, 102–115.
• Casella, G. and Robert, C.P. A History of Markov Chain Monte Carlo. In MCMC Handbook, 49–66. arXiv:0808.2902
• Chopin, N., Iacobucci, A., Marin, J.-M., Mengersen, K.L., Robert, C.P., and Ryder, R.J. Discussions on Particle learning by Lopes et al. Bayesian Statistics 9, 355–359. arXiv:1006.0554
• Cornuet, J.-M., Marin, J.-M., Pillai, N.S., and Robert, C.P. Lack of confidence in approximate Bayesian computational (ABC) model choice. Proceedings of the National Academy of Sciences, 108, 15112–15117. arXiv:1102.4432
• Douc, R. and Robert, C.P. A vanilla Rao-Blackwellisation of Metropolis-Hastings algorithms. Annals of Statistics, 39(1), 261–277. arXiv:0904.2144 · link
• Jacob, P.E., Robert, C.P., and Smith, M. Using parallel computation to improve independent Metropolis-Hastings based estimation. J. Computat. Graphical Statistics, 20, 616–635. arXiv:1010.1595 · link
• Marin, J.-M., Pudlo, P., Robert, C.P., and Ryder, R.J. Approximate Bayesian Computational methods. Statist. Computing, 21, 289–291. arXiv:1101.0955
• Marin, J.-M., Robert, C.P., and Rousseau, J. Bayesian Inference and computation. In Statistical System Biology, 39–65. link
• 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: In Honor of James O. Berger, 513–527. arXiv:0910.2325
• Marin, J.-M. and Robert, C.P. On computational tools for Bayesian Data Analysis. In Bayesian Methods and Expert Elicitation. arXiv:1002.2684
• Mengersen, K.L., Robert, C.P., and Titterington, M.T. Mixtures: Estimation and Applications. John Wiley, Chichester.
• Robert, C.P. An attempt at reading Keynes' Treatise on Probability. International Statistical Review, 79(1), 1–15. arXiv:1003.4455
• Robert, C.P. Numerical Analysis for Statisticians: a review. International Statistical Review, 79, 502–503. link
• Robert, C.P. The foundations of Statistics: a simulation-based approach: a review. International Statistical Review, 79, 486–487. arXiv:1105.4823 · link
• Robert, C.P. A handbook of statistical analyses with R. link
• Robert, C.P. Evidence and Evolution: a review. Human Genomics, 5, 130–136. arXiv:1004.5074
• Robert, C.P. Anathem, by Neal Stephenson. CHANCE, book review, 24(4), 60–61. link
• Robert, C.P., Marin, J.-M., and Pillai, N.S. Why approximate Bayesian computational (ABC) methods cannot handle model choice problems. arXiv:1101.5091
• Robert, C.P. James Gentle: Computational Statistics: a review. Statistics and Computing, 21(2), 289–291. link
• Robert, C.P. Numerical Analysis for Statisticians, by Kenneth Lange. CHANCE, book review, 24(4), 58–59.
• Robert, C.P. and Rousseau, J. On Bayesian Data Analysis. In Bayesian Methods and Expert Elicitation. arXiv:1001.4656 · link
• Robert, C.P. and Rousseau, J. Discussion on Bernardo's Integrated objective Bayesian estimation and hypothesis testing. Bayesian Statistics 9, 58–59. link
• Robert, C.P. and Rousseau, J. Discussion on Consonni and LaRocca's On moment priors for Bayesian model choice. Bayesian Statistics 9, 142–143. link
• Robert, C.P. The foundations of Statistics: a simulation-based approach by Shravan Vasishth and Michael Broe. CHANCE, book review, 24(4), 59–60.

2010

• Balding, D.A., Beaumont, M.A., Beerli, P., Chikhi, L., Coranders, J., Cornuet, J.-M., Estoup, A., Excoffier, L., Fagundes, N., Foll, M., Gaggiotti, O., Hey, J., Hickerson, M., Huelsenbeck, J., Knowles, L., Mahesh, P., Nielsen, R., Robert, C.P., Rousset, F., Sisson, S., Vitalis, R., and Yang, Z. In defense of model-based inference in phylogeography. Molecular Ecology, 19, 436–446.
• Benabed, K., Bouchet, F., Cappé, O., Cardoso, J.-F., Fort, G., Kilbinger, M., Prunet, S., Robert, C.P., and Wraith, D. Bayesian model comparison in cosmology with population Monte Carlo. Monthly Notices of the Royal Astronomical Society: Letters, 405, 2381–2390. arXiv:0912.1614
• Berger, J.O., Fienberg, S., Raftery, A., and Robert, C.P. Letter on Incoherent Phylogeographic Inference. Proc. National Academy Sciences, 107, E157. arXiv:1006.3854 · link
• Casella, G. and Robert, C.P. Generating random variables. In StatProb: The Encyclopedia Sponsored by Statistics and Probability Societies. link
• Casella, G. and Robert, C.P. Report of the Editors. J. Royal Statistical Society Series B, 72, 1–2.
• Casella, G. and Robert, C.P. Solutions to odd-numbered exercises of Introducing Monte Carlo Methods with R. arXiv:1001.2906
• Chopin, N., Jacob, P.E., Robert, C.P., and Rue, H. Discussion of ``Particle Markov chain Monte Carlo methods" by C. Andrieu, A. Doucet and R. Hollenstein. J. Royal Statistical Society, 72, 411–412. arXiv:0911.0985
• Chopin, N. and Robert, C.P. Properties of nested sampling. Biometrika, 97, 741–755. arXiv:0801.3887 · link
• Chopin, N., Robert, C.P., and Rousseau, J. Rejoinder: Harold Jeffreys's Theory of Probability Revisited. Statistical Science, 24(2), 191–194. arXiv:0909.1008
• Iaccobucci, A., Marin, J.-M., and Robert, C.P. On variance stabilisation by double Rao-Blackwellisation. Computational Statistics and Data Analysis, 54, 698–710. arXiv:0802.3690
• Marin, J.-M. and Robert, C.P. On resolving the Savage-Dickey paradox. Electronic Journal of Statistics, 4, 643–654. arXiv:0910.1452
• Robert, C.P. On the relevance of the Bayesian approach to Statistics. Review of Economic Analysis, 2, 139–152. arXiv:0909.5369
• Robert, C.P. A Search for Certainty: a critical assessment. Bayesian Analysis, 5(2), 213–222. arXiv:1001.5109

2009

• Beaumont, M.A., Cornuet, J.-M., Marin, J.-M., and Robert, C.P. Adaptivity for ABC algorithms: the ABC-PMC scheme. Biometrika, 54, 698–710. arXiv:0805.2256
• Benabed, K., Cappé, O., Cardoso, J.-F., Fort, G., Kilbinger, M., Prunet, S., Robert, C.P., and Wraith, D. Estimation of cosmological parameters using adaptive importance sampling. Physical Review D, 80, 023502. arXiv:0903.0837
• Cappé, O., Douc, R., Guillin, A., Marin, J.-M., and Robert, C.P. Adaptive importance sampling in general mixture classes. Statistics and Computing, 18, 447–459. arXiv:0710.4242
• Casella, G. and Robert, C.P. Introducing Monte Carlo Methods with R. Springer-Verlag, New York. arXiv:0909.0389 · link
• Casella, G. and Robert, C.P. Report of the editors. J. Royal Statistical Society, 71, 1–2.
• Chen, C., Mengersen, K.L., and Robert, C.P. Letter on Model choice versus model criticism. Proc. Nat. Acad. Sciences, 107, E5. arXiv:0909.5673
• Chopin, N., Robert, C.P., and Rousseau, J. Harold Jeffreys' Theory of Probability revisited. Statistical Science, 24, 141–194. arXiv:0804.3173
• Cucala, J., Marin, J.-M., Robert, C.P., and Titterington, M.T. A Bayesian reassessment of nearest-neighbour classification. J. American Statistical Association, 104(485), 263–273. arXiv:0802.1357
• 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. arXiv:0807.2767
• Lee, A., Marin, J.-M., Mengersen, K.L., and Robert, C.P. Bayesian Inference on Mixtures of Distributions. In Perspectives in Mathematical Sciences I, 165–202. arXiv:0804.2413
• Marin, J.-M. and Robert, C.P. Les bases de la statistique bayésienne. In Techniques de l'Ingénieur. link
• Robert, C.P. Invited discussion of ``Natural induction: An objective Bayesian approach" by J.O. Berger, J.M. Bernardo and D. Sun. Rev. Acad. Sci. Madrid, A 103, 149–150.
• Robert, C.P. mcsm, with R programs used in the book ``Introducing Monte Carlo Statistical Methods".
• Robert, C.P. and Wraith, D. Computational methods for Bayesian model choice. AIP Conference Proceedings, 1193, 251–262. arXiv:0907.5123

2008

• Balding, D.A., Beaumont, M.A., Cornuet, J.-M., Estoup, A., Guillemaud, T., Marin, J.-M., Robert, C.P., and Santos, F. Infering population history with DIY ABC: a user-friendly approach to Approximate Bayesian Computation. Bioinformatics, 24, 2713–2719. arXiv:0804.4372
• Casarin, R., Marin, J.-M., and Robert, C.P. Discussion of ``Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by H. Rue, S. Martino, and N. Chopin. J. Royal Statistical Society, 71, 360–361. arXiv:1002.2080
• Casarin, R. and Robert, C.P. Discussion of ``Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by H. Rue, S. Martino, and N. Chopin. J. Royal Statistical Society, 71, 359–360.
• Jouini, E., Mansour, S.B., Marin, J.-M., Napp, C., and Robert, C.P. Are risk agents more optimistic? A Bayesian estimation approach. Journal of Applied Econometrics, 23, 843–860.
• Marin, J.-M. and Robert, C.P. On some difficulties with some posterior probability approximations. Bayesian Analysis, 3, 427–442. arXiv:0801.3513
• Marin, J.-M. and Robert, C.P. Approximating the marginal likelihood in mixture models. In Bulletin of the Indian Chapter of ISBA, 2–7. arXiv:0804.2414
• Robert, C.P. Invited discussion of ``Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by H. Rue, S. Martino, and N. Chopin. J. Royal Statistical Society, 71, 355–357.
• Robert, C.P. Discussion of ``Sure independence screening for ultrahigh dimensional feature space" by J. Fan and L. Lv. J. Royal Statistical Society, 70.
• Robert, C.P. A propos de "Bayésiens contre fréquentistes, un faux débat" de N. Vayatis. La Recherche, 424.
• Robert, C.P. and Wood, A. Report of the Editors. J. Royal Statistical Society Series B, 70, 1–2.

2007

• Alston, C.L., Ball, A.J., Littlefield, P.J., Mengersen, K.L., Perry, D., Robert, C.P., and Thompson, M. Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images. Computational Statistics and Data Analysis, 51(9), 4282–4296.
• Chopin, N. and Robert, C.P. Contemplating Evidence: properties, extensions of, and alternatives to Nested Sampling. arXiv:0801.3887
• Douc, R., Guillin, A., Marin, J.-M., and Robert, C.P. Convergence of adaptive sampling schemes. Annals of Statistics, 35, 420–448. arXiv:0708.0711
• 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.
• Kendall, W., Marin, J.-M., and Robert, C.P. Confidence bands for simulation output. Statistics and Computing, 17, 1–10.
• Marin, J.-M. and Robert, C.P. Bayesian Core. Springer-Verlag, New York.
• Marin, J.-M. and Robert, C.P. The complete solution manual to Bayesian Essentials with R. arXiv:1503.04662
• Marin, J.-M. and Robert, C.P. The complete solution manual to Bayesian Core. arXiv:0910.4696 · link
• Robert, C.P. Discussion of ``Splitting and merging components of a nonconjugate Dirichlet process mixture model" by A. Jain and R. Neal. Bayesian Analysis, 2, 479–482.
• Robert, C.P. and Wood, A. Report of the Editors. J. Royal Statistical Society Series B, 69, 1–2.

2006

• Amzal, B., Bois, F., Parent, E., and Robert, C.P. Bayesian optimal design via interacting MCMC. J. American Statist. Association, 101, 773–785.
• Cano, J.A., Robert, C.P., and Salmerón, D. Integral equation solutions as prior distributions for model selection. TEST, 13, 445–463.
• Celeux, G., Forbes, F., Robert, C.P., and Titterington, M.T. Deviance criteria in missing data models. Bayesian Analysis, 1, 651–674.
• Celeux, G., Marin, J.-M., and Robert, C.P. Sélection bayésienne de variables en régression linéaire. J. Société Française de Statistique, 147, 1–26.
• Chopin, N. and Robert, C.P. Discussion of ``Nested sampling for Bayesian computations" by J. Skilling. Proc. Eight Valencia Conference on Bayesian Statistics.
• Hobert, J.O., Jones, G.L., and Robert, C.P. Using a Markov chain to construct a tractable approximation of an untractable probability distribution. Scandin. Statist, 33, 37–61.
• Müller, P., Robert, C.P., and Rousseau, J. Sample Size Choice for Microarray Experiments. In Bayesian Inference for Gene Expression and Proteomics.
• Robert, C.P. Le Choix Bayésien : Principes et Pratique.
• Robert, C.P. Invited discussion of ``On the frequentist and Bayesian approaches to hypothesis testing" by E. Moreno and F.J. Girón. SORT, 30, 40–45.
• Robert, C.P. Discussion of ``Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes" by Beskos, Papaspiliopoulos, Roberts, and Fearnhead. J. Royal Statistical Society, 68, 378–379.
• Robert, C.P. Gaussian Markov Random Fields (Theory and Applications): a review. Statistics in Medicine, 25, 3025–3026.

2005

• Celeux, G., Marin, J.-M., and Robert, C.P. Iterated importance sampling in missing data problems. Computational Statistics and Data Analysis, 50, 3386–3404.
• Guillin, A., Marin, J.-M., and Robert, C.P. Estimation bayésienne approximative par échantillonnage prééférentiel. Revue de Statistique Appliquée, LIII, 79–95.
• Marin, J.-M., Mengersen, K.L., and Robert, C.P. Bayesian Estimation of Mixture Models. In Handbook of Statistics 25, Chapter 16, 459–507.
• Robert, C.P. and Rydén, T. Fully Bayesian approaches. In Inference in Hidden Markov Models.

2004

• Andrieu, C., Doucet, A., and Robert, C.P. Computational advances for and from Bayesian analysis. Statistical Science, 19, 118–127.
• Cappé, O., Guillin, A., Marin, J.-M., and Robert, C.P. Population Monte Carlo. J. Computational and Graphical Statistics, 13, 907–929.
• Casella, G. and Robert, C.P. Statistical Monte-Carlo Methods. Springer-Verlag, New York.
• Casella, G., Robert, C.P., and Wells, M.T. Generalized Accept-Reject sampling schemes. In Festschrift for Hermann Rubin, 342–347.
• Casella, G. and Robert, C.P. Introduction to the Special Issue: Bayes Then and Now. In Statistical Science, 1–2.
• Fienberg, S. and Robert, C.P. Discussion of ``Ecological Inference for 2x2 tables" by J. Wakefield. J. Royal Statistical Society, 66.
• Hobert, J.O. and Robert, C.P. A mixture representation of with applications in Markov chain Monte Carlo and perfect sampling. Annals of Applied Probability, 14, 1295–1305.
• Marin, J.-M., Mengersen, K.L., and Robert, C.P. Bayesian modelling and inference on mixtures of distributions. In Atti della XLII Riunione Scientifica della Societa Italiana di Statistica, Universita di Bari, 247–255.
• Mueller, P., Parmigiani, G., Robert, C.P., and Rousseau, J. Optimal sample size for multiple testing: the case of gene expression microarrays. J. American Statistical Association, 99, 990–1001.
• Robert, C.P. The Bayesian Choice: From decision-theoretic foundations to computational implementation. Springer-Verlag, New York (DeGroot Prize 2004).
• Robert, C.P. Special Issue on Bayesian Statistics Today. Statistical Science.
• Robert, C.P. Bayesian Computational Methods. In Handbook of Computational Statistics, 719–766.
• Robert, C.P. Invited discussion at the Half Day on Inverse Problems. J. Royal Statistical Society, 66.

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 Statistical Society, 65(3), 679–700.
• Dupuis, J. and Robert, C.P. Model choice in qualitative regression models. J. Statistical Planning and Inference, 111, 77–94.
• Hurn, M., Justel, A., and Robert, C.P. Estimating mixtures of regressions. J. Computational and Graphical Statistics, 12, 1–25.
• Philippe, A. and Robert, C.P. Perfect simulation of positive Gaussian distributions. Statistics and Computing, 13, 179–186.
• Robert, C.P. Invited discussion of ``Statistical Models for Monte Carlo Integration" by A. Kong, P. McCullagh, D. Nicolae, Z. Tan, and X.L. Meng. J. Royal Statistical Society, 65.
• Robert, C.P. Invited discussion of ``Efficient construction of reversible jump MCMC proposals" by S. Brooks, P. Giudici and G.O. Roberts. J. Royal Statistical Society, 65, 39–42.
• Robert, C.P. Discussion of ``Advances in MCMC" by G.O. Roberts. Highly Structured Stochastic Systems.

2002

• Cappé, O., Douc, R., Moulines, E., and Robert, C.P. On the convergence of Monte Carlo maximum likelihood simulations for latent variable models. Scandinavian J. Statistics, 29, 615–636.
• Casella, G., Mengersen, K.L., Robert, C.P., and Titterington, M.T. Perfect slice sampling for mixtures of distributions. J. Royal Statistical Society, 64, 777–790.
• Doucet, A., Godsill, S., and Robert, C.P. Marginal Maximum a Posteriori Estimation using Markov Chain Monte Carlo. Statistics and Computing, 12, 77–84.
• Iorio, M.D. and Robert, C.P. Discussion of ``Bayesian measures of complexity and fit" by D.J. Spiegelhalter, N.G. Best, B.P. Carlin and A. van der Linde. J. Royal Statistical Society, 64, 629–630.
• Marin, J.-M. and Robert, C.P. Discussion of ``Chain graph models and their causal interpretation" by S.L. Lauritzen and T.S. Richardson. J. Royal Statistical Society, 64.
• Mengersen, K.L. and Robert, C.P. Iid sampling with self-avoiding particle filters: the pinball sampler. In Proc. Seventh Valencia Conference on Bayesian Statistics.
• Robert, C.P. Techniques de calcul et simulation. In Méthodes Statistiques Bayésiennes, Chapter 5.
• Robert, C.P. Fondements decisionnels de l'analyse bayésienne. In Méthodes Statistiques Bayésiennes.
• Robert, C.P. Modèles de mélanges. In Méthodes Statistiques Bayésiennes.
• Robert, C.P. Séries temporelles. In Méthodes Statistiques Bayésiennes.
• Robert, C.P. Discussion of ``Bayesian Clustering with Variables and Transformation Selections" by J. Liu, J. Zhang, M. Palumbo and C. Lawrence. Proc. Seventh Valencia Conference on Bayesian Statistics.
• Robert, C.P. Discussion of ``Identifying Mixtures of Regression Equations by the SAR Procedure" by D. Peñ a, J. Rodriguez and G. Tiao. Proc. Seventh Valencia Conference on Bayesian Statistics.
• Robert, C.P. and Rousseau, J. Discussion of ``Bayesian and Frequentist Multiple Testing" by C. Genovese and L. Wasserman. Proc. Seventh Valencia Conference on Bayesian Statistics.
• Robert, C.P. and Titterington, M.T. Discussion of ``Bayesian measures of complexity and fit" by D.J. Spiegelhalter, N.G. Best, B.P. Carlin and A. van der Linde. J. Royal Statistical Society, 64, 621–622.

2001

• Altaleb, A. and Robert, C.P. Analyse bayésienne du modèle logit : algorithmes par tranches ou Metropolis-Hastings. Revue de Statistique Appliquée, 49, 53–70.
• Casella, G., Lavine, M., and Robert, C.P. Explaining the perfect sampler. The American Statistician, 55, 299–305.
• Philippe, A. and Robert, C.P. Riemann sums for MCMC estimation and convergence monitoring. Statistics and Computing, 11, 103–115.

2000

• Cappé, O. and Robert, C.P. MCMC: Ten years and still running!. J. American Statistical Association, 95, 1282–1286.
• Celeux, G., Hurn, M., and Robert, C.P. Computational and inferential difficulties with mixtures posterior distribution. J. American Statistical Association, 95, 957–979.
• 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, 43–54.
• Robert, C.P. Préface de. L'analyse statistique pour l'environnement.
• Robert, C.P. Computer-Assisted Analysis of Mixtures and Applications: a review. J. American Statistical Association, 95.
• Robert, C.P., Rydén, T., and Titterington, M.T. Jump Markov chain Monte Carlo algorithms for Bayesian inference in hidden Markov models. J. Royal Statistical Society, 62, 57–75.
• Robert, C.P. and Zakoian, J.M. Modèles de chaînes de Markov cachées. Lettre du Crest, 24.

1999

• Billio, M., Monfort, A., and Robert, C.P. Bayesian estimation of switching ARMA models and consequences on ML estimation. J. Econometrics, 93, 229–255.
• Casella, G. and Robert, C.P. Statistical Monte-Carlo Methods. Springer-Verlag, New York.
• Doucet, A., Godsill, S., and Robert, C.P. Marginal MAP Estimation using Markov Chain Monte Carlo. In Proc. IEEE-ICASP'99.
• Dupuis, J. and Robert, C.P. Bayesian variable selection in qualitative models by Kullback-Leibler projections. In Proc. Workshop on Model Selection, 275–305.
• Gruet, M.A., Philippe, A., and Robert, C.P. Estimation of the number of components in a mixture of exponential distributions. J. Computational and Graphical Statist, 8, 298–317.
• Guihenneuc-Jouyaux, C., Mengersen, K.L., and Robert, C.P. MCMC Convergence Diagnostics: a ``Reviewww". In Bayesian Statistics 6, (eds. Bernardo, J.M. and Berger, J.O. and Dawid, A.P. and Smith, A.F.M.), 415–440. Oxford University Press, Oxford.
• Hobert, J.O. and Robert, C.P. Admissibility in exponential families, null recurrence of Markov chains, and drift conditions. Annals of Statistics, 27, 361–373.
• Hobert, J.O., Robert, C.P., and Titterington, M.T. On perfect simulation for some mixtures of distributions. Statistics and Computing, 9, 287–298.
• Robert, C.P. Improving Efficiency by Shrinkage: a review. Mathematical Reviews.
• Robert, C.P., Rydén, T., and Titterington, M.T. Convergence controls for MCMC algorithms, with applications to hidden Markov chains. J. Statistical Computation and Simulation, 64, 327–355.
• Robert, C.P. and Saleh, A. Recentered confidence sets: a review. In Zeitschrift for Professor P.K. Sen.

1998

• Berger, J.O., Philippe, A., and Robert, C.P. Estimating quadratic functions: reference priors for non-centrality parameters. Statistica Sinica, 8, 359–375.
• Cappé, O., Douc, R., Moulines, E., and Robert, C.P. Bayesian analysis of overdispersed count data with applications to teletraffic monitoring. In COMPSTAT 1998, 215–220.
• Casella, G. and Robert, C.P. Post-Processing Accept-Reject Samples: Recycling and Rescaling. J. Computational and Graphical Statistics, 7, 139–157.
• Cellier, D. and Robert, C.P. Convergence Control of MCMC Algorithms. In Discretization and MCMC Convergence Assessment, 27–46.
• Chauveau, D., Diebolt, J., and Robert, C.P. Control by the Central Limit Theorem. In Discretization and MCMC Convergence Assessment, 99–126.
• Fraisse, A.M., Robert, C.P., and Roy, M. Semi-tail upper bounds for admissible estimators in exponential families with nuisance parameters. Statistics & Decisions, 16, 147–162.
• Goutis, C. and Robert, C.P. Model Choice in generalized linear models: a Bayesian approach via Kullback-Leibler projections. Biometrika, 85, 29–37.
• Gruet, M.A., Philippe, A., and Robert, C.P. Estimation of Exponential Mixtures. In Discretization and MCMC Convergence Assessment, 161–174.
• Guihenneuc-Jouyaux, C. and Robert, C.P. Finite Markov chain convergence results and MCMC convergence assessment. J. American Statistical Association, 93, 1055–1067.
• Guihenneuc-Jouyaux, C. and Robert, C.P. Valid Discretization and Renewal Theory. In Discretization and MCMC Convergence Assessment, 67–98.
• Mengersen, K.L. and Robert, C.P. Reparametrization issues in mixture estimation and their bearings on the Gibbs sampler. Data Analysis and Computational Statistics, 29, 325–343.
• Philippe, A. and Robert, C.P. A note on the confidence properties of reference priors for the calibration model. TEST, 7, 147–160.
• Philippe, A. and Robert, C.P. Linking Discrete and Continuous Chains. In Discretization and MCMC Convergence Assessment, 47–66.
• Reber, A. and Robert, C.P. Bayesian Modelling of a Biopharmaceutical Experiment with Heterogeneous Responses. Sankhya, 60, 145–160.
• Richardson, S. and Robert, C.P. Markov Chain Monte Carlo methods. In Discretization and MCMC Convergence Assessment, 1–26.
• Robert, C.P. Discretization and MCMC Convergence Assessment. Springer-Verlag, New York (Lecture Notes 135).
• Robert, C.P. A pathological Metropolis algorithm and its use as a benchmark for convergence assessment techniques. J. Computational Statistics, 13, 169–184.
• Robert, C.P. MCMC Specifics for Latent Variable Models. In COMPSTAT 1998, 101–112.
• Robert, C.P. Two techniques of integration by parts and some applications. In Applied Statistical Science, III, 175–191.
• Robert, C.P. Discussion of ``Nested Hypothesis Testing: The Bayesian Reference Criterion" by J.M. Bernardo. Bayesian Statistics 6.
• Robert, C.P. Discussion of ``On the Dangers of Modelling Through Continuous Distributions: A Bayesian Perspective" by C. Fernández and M. Steel. Bayesian Statistics 6.
• Robert, C.P. Contrôle de convergence dans les méthodes de simulation par chaînes de Markov. Lettre du Crest, 15.
• Robert, C.P. and Titterington, M.T. Reparameterisation strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation. Statistics and Computing, 8, 145–158.
• Robert, C.P. and Titterington, M.T. Discussion of ``Perfect simulation methods'' by P.J. Green and D. Murdoch. Bayesian Statistics 6.

1997

• Bensmail, H., Celeux, G., Raftery, A., and Robert, C.P. Inference in geometric model-based cluster analysis. Statistics and Computing, 7, 1–10.
• Goutis, C., Hobert, J.O., and Robert, C.P. A connectedness condition for the convergence of the Gibbs sampler. Statistics & Probability Letters, 33, 235–240.
• Goutis, C. and Robert, C.P. Selection between hypotheses using estimation criteria. Annales d'Economie et Statistique, 46, 1–22.
• Gruet, M.A. and Robert, C.P. Contributed discussion of ``On Bayesian analysis of mixtures with an unknown number of components" by S. Richardson and P. Green. J. Royal Statistical Society, 59. arXiv:1010.6113
• Mc Hugh, M., Robert, C.P., Tarisse, F., Vernotte, F., and Zalamansky, G. Search of a Gravitational Wave Background in timing residuals of PSR1931+21: minimal noise model and upper limits on _gr. Monthly Notices of the Royal Astronomical Association, 288, 533–537.
• Robert, C.P. Invited discussion of ``On Bayesian analysis of mixtures with an unknown number of components" by S. Richardson and P. Green. J. Royal Statistical Society, 59, 758–764.

1996

• Caron, N. and Robert, C.P. Noninformative Bayesian testing and neutral Bayes factors. TEST, 5, 411–437.
• Casella, G. and Robert, C.P. Rao-Blackwellisation of sampling schemes. Biometrika, 83, 81–94.
• Casella, G. and Robert, C.P. Une implémentation du Théorème de Rao-Blackwell en simulation avec rejet. Notes aux Comptes Rendus de l'Académie des Sciences, 322, 571–576.
• Casella, G. and Robert, C.P. Obituary of Constantinos Goutis. IMS Bulletin, 25.
• Hwang, G. and Robert, C.P. Maximum likelihood estimation under order restrictions by the Prior Feedback method. J. American Statistical Association, 91, 167–173.
• Mengersen, K.L. and Robert, C.P. Testing for mixtures: a Bayesian entropy approach. In Proc. Fifth Valencia Conference on Bayesian Statistics, 255–276.
• Robert, C.P. Méthodes de Monte Carlo par chaînes de Markov.
• Robert, C.P. Intrinsic losses. Theory and Decision, 40, 191–214.
• Robert, C.P. Mixtures of distributions: inference and estimation. In Markov Chain Monte Carlo in Practice, 441–464.
• Robert, C.P. Discussion of ``Convergence of Markov Chain Monte Carlo algorithms" by N. Polson. Bayesian Statistics 5, 315–316.
• Robert, C.P. Faut-il accepter ou rejeter les p-values ?. J. Société Statistique de Paris, 137, 39–49.

1995

• Casella, G. and Robert, C.P. Recyclage dans les méthodes d'acceptation-rejet. Notes aux Comptes Rendus de l'Académie des Sciences, 321, 1621–1626.
• Casella, G. and Robert, C.P. Discussion of ``Accurate restoration of DNA sequences" by G. Churchill. Bayesian Case Studies II, 126–138.
• Fourdrinier, D. and Robert, C.P. A note on empirical Bayes via entropy. Statistics & Probability Letters, 23, 35–44.
• Robert, C.P. Convergence control techniques for Monte Carlo Markov Chain algorithms. Statistical Science, 10, 231–253.
• Robert, C.P. Simulation of truncated normal variables. Statistics and Computing, 5, 121–125. arXiv:0907.4010

1994

• Casella, G. and Robert, C.P. Distance penalized losses for testing and confidence set evaluation. TEST, 3, 163–182.
• Diebolt, J. and Robert, C.P. Estimation of finite mixture distributions through Bayesian sampling. J. Royal Statistical Society, 56, 363–375.
• Goutis, C. and Robert, C.P. Discussion of ``An overview of Robust Bayesian Analysis" by J.O. Berger. TEST, 3, 103–107.
• Kubokawa, T. and Robert, C.P. New perspectives on linear calibration. J. Multivariate Analysis, 51, 178–200.
• Robert, C.P. The Bayesian Choice: a Decision-Theoretic Motivation. Springer-Verlag, New York.
• Robert, C.P. Méthodes de simulation par chaînes de Markov : une introduction. Lettre du Crest, 6.
• Robert, C.P. Discussion of ``Markov Chain for exploring posterior distributions" by L. Tierney. Annals of Statistics, 22, 1722–1727.

1993

• Casella, G., Hwang, G., and Robert, C.P. A paradox in decision-theoretic interval estimation. Statistica Sinica, 3, 141–155.
• Casella, G., Hwang, G., and Robert, C.P. Loss functions for set estimation. In Stat. Dec. Theory and Rel. Topics V, 237–257.
• Casella, G. and Robert, C.P. Domination of the constant confidence statement for the usual normal confidence set. In Stat. Dec. Theory and Rel. Topics V, 351–368.
• Celeux, G., Diebolt, J., and Robert, C.P. Bayesian estimation of Hidden Markov Models: a stochastic implementation. Statistics & Probability Letters, 16, 77–83.
• Diebolt, J. and Robert, C.P. Discussion of ``Bayesian computations via the Gibbs sampler" by A.F.M. Smith and G. Roberts. J. Royal Statistical Society, 55, 71–72.
• Kubokawa, T., Robert, C.P., and Saleh, A. Estimation of noncentrality parameters. Canadian Journal of Statistics, 21, 45–58.
• Robert, C.P. Prior Feedback: Bayesian tools to maximum likelihood estimation. J. Computational Statistics, 8, 279–294.
• Robert, C.P. A Note on Jeffreys-Lindley paradox. Statistica Sinica, 3, 601–608.
• Robert, C.P. and Soubiran, C. Estimation of a mixture model through Bayesian sampling and prior feedback. TEST, 2, 125–146.

1992

• Berliner, M. and Robert, C.P. Discussion of ``Who knows what alternative lurks in the heart of significance tests?'' by J.S. Hodges. Bayesian Statistics 4.
• Casella, G., Farrell, R., Hwang, G., Robert, C.P., and Wells, M.T. Estimation of accuracy in testing. Annals of Statistics, 20, 490–509.
• George, E. and Robert, C.P. Calculating Bayes estimates for capture-recapture models. Biometrika, 41, 677–683.
• Hwang, G., Robert, C.P., and Strawderman, W. Is Pitman closeness a reasonable criterion?. J. American Statistical Association, 88, 57–76.
• Kubokawa, T., Robert, C.P., and Saleh, A. Empirical Bayes estimation of the covariance matrix of a normal distribution with unknown mean under an entropy loss. Sankhya, 54, 402–410.
• Robert, C.P. L'Analyse Statistique Bayésienne.
• Robert, C.P. Invited discussion of ``Recent extensions to the EM algorithm'' by X. Meng and D. Rubin. Bayesian Statistics 4.

1991

• Celeux, G., Diebolt, J., Robert, C.P., and Soubiran, C. Estimation de mélanges pour de petits échantillons: application à la cinématique stellaire. Revue de Statistique Appliquée, 39, 17–36.
• Kubokawa, T., Robert, C.P., and Saleh, A. Robust estimation of common regression coefficients under spherical symmetry. Annals of the Institute of Statistical Mathematics, 43, 121–126.
• Robert, C.P. Generalized Inverse Normal distributions. Statistics & Probability Letters, 11, 37–41.
• Robert, C.P. and Saleh, A. Point estimation and confidence set estimation in a parallelism model: an empirical Bayes approach. Annales d'Economie et Statistique, 23, 65–89.

1990

• Berger, J.O. and Robert, C.P. Subjective hierarchical Bayes estimation of a multivariate normal mean: on the frequentist interface. Annals of Statistics, 18, 617–651.
• Casella, G. and Robert, C.P. Improved Confidence Sets in Spherically Symmetric Distributions. J. Multivariate Analysis, 32, 84–94.
• Diebolt, J. and Robert, C.P. Estimation des paramètres d'un mélange par échantillonnage bayésien. Notes aux Comptes Rendus de l'Académie des Sciences, 311, 653–658.
• Fraisse, A.M., Raoult, J.P., Robert, C.P., and Roy, M. Une condition nécessaire d'admissibilité pour les familles exponentielles et ses conséquences sur les estimateurs à rétrécisseur de la moyenne d'un vecteur normal. Revue Canadienne de Statistique, 18(1), 213–220.
• Robert, C.P. On some accurate bounds for the quantiles of a non-central chi squared distribution. Statistics & Probability Letters, 10, 101–106.
• Robert, C.P. Modified Bessel functions and their applications in Probability and Statistics. Statistics & Probability Letters, 9, 155–161.

1989

• Casella, G. and Robert, C.P. Refining Poisson confidence intervals. Canadian Journal of Statistics, 17, 45–52.
• Casella, G. and Robert, C.P. De meilleures régions de confiance pour les lois à symétrie sphérique. Notes aux Comptes Rendus de l'Académie des Sciences, 308, 233–236.
• Cellier, D., Fourdrinier, D., and Robert, C.P. Robust shrinkage estimators of the location parameter for elliptically symmetric distributions. J. Multivariate Analysis, 29, 39–52.
• Cellier, D., Fourdrinier, D., and Robert, C.P. Controlled shrinkage estimators. Statistics, 20, 13–22.
• Robert, C.P. A lower bound for the risk of a shrinkage estimator and some deduced estimators. Communications in Statistics, 18, 2289–2299.
• Robert, C.P. Decision Analysis: a Bayesian approach: a review. J. American Statistical Association, 84.

1988

• Robert, C.P. Performance d'estimateurs à rétrécisseur en situation de multicolinéarité. Annales d'Economie et Statistique, 10, 97–119.
• Robert, C.P. An explicit formula for the risk of the positive-part James-Stein estimator. Canadian Journal of Statistics, 16, 161–168.

1987

• Cellier, D., Fourdrinier, D., and Robert, C.P. Estimateurs à rétrécisseur du paramètre de position d'une loi à symétrie sphérique. Notes aux Comptes Rendus de l'Académie des Sciences, 304, 439–442.
• Fraisse, A.M., Robert, C.P., and Roy, M. Estimateurs à rétrécisseur matriciel différentiable, pour un coût quadratique général. Annales d'Economie et Statistique, 8, 161–175.
• Fraisse, A.M., Robert, C.P., and Roy, M. Une condition nécessaire d'admissibilité pour les familles exponentielles et ses conséquences sur les estimateurs à rétrécisseur matriciel. Notes aux Comptes Rendus de l'Académie des Sciences, 305, 889–891.