Christian P. Robert

Selected publications/preprints

2014

Banterlé, 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, . Available

as arxiv:1110.4700

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) Available asarxiv:1403.4359

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

Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gauthier, M. and Robert,

C.P. Reliable ABC model choice via random forests. Available

as arXiv:1406.6288

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

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

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 asarXiv:1406.5384

Salmeron, D., Cano, J.A., and Robert, C.P. Objective Bayesian hypothesis

testing in binomial regression models with integral prior

distributions. Statistica Sinica (to appear). Available

asarxiv.org/abs/1306.6928.

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 asarXiv: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

Lee, K. and Robert, C.P. Importance sampling schemes for evidence

approximation in mixture models. Available as arxiv:1311.600

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 asarXiv: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 asarXiv: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 asarXiv: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 asarXiv: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 asPDF 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 asCompressed 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 asPostscript.

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,