2022

Elvira, Victor, Martino, Luca and Robert, Christian P. (2022) Rethinking the effective sample size. International Statistical Review . doi:10.1111/insr.12500 (In Press)

2021

Clarté, Grégoire, Robert, Christian P., Ryder, Robin and Stoehr, Julien (2021) Component-wise approximate Bayesian computation via Gibbs-like step. Biometrika, 108 (3). pp. 591-607. asaa090. doi:10.1093/biomet/asaa090

2020

Drovandi, Christopher C., Grazian, Clara, Mengersen, Kerrie and Robert, Christian P. (2020) Approximating the likelihood in approximate Bayesian computation. In: Sisson, Scott A. and Fan, Yanan and Beaumont, Mark, (eds.) Handbook of Approximate Bayesian Computation. Handbooks of Modern Statistical Methods . Boca Raton, Florida: CRC Press. ISBN 9781439881507

Frazier, David T., Robert, Christian P. and Rousseau, Judith (2020) Model misspecification in approximate Bayesian computation : consequences and diagnostics. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82 (2). pp. 421-444. doi:10.1111/rssb.12356

2019

Bernton, Espen, Jacob, Pierre E., Gerber, Mathieu and Robert, Christian P. (2019) On parameter estimation with the Wasserstein distance. Information and Inference, 8 (4). pp. 657-676. doi:10.1093/imaiai/iaz003

Banterle, Marco, Grazian, Clara, Lee, Anthony and Robert, Christian P. (2019) Accelerating Metropolis - Hastings algorithms by delayed acceptance. Foundations of Data Science, 1 (2). pp. 103-128. doi:10.3934/fods.2019005

Raynal, Louis, Marin, Jean-Michel, Pudlo, Pierre, Ribatet, Mathieu, Robert, Christian P. and Estoup, Arnaud (2019) ABC random forests for Bayesian parameter inference. Bioinformatics, 35 (10). pp. 1720-1728. doi:10.1093/bioinformatics/bty867

Bernton, Espen, Jacob, Pierre, Gerber, Mathieu and Robert, Christian P. (2019) Approximate Bayesian computation with the Wasserstein distance. Journal of the Royal Statistical Society Series B: Statistical Methodology, 81 (2). pp. 235-269. doi:10.1111/rssb.12312

McShane, Blakeley, Gal, David, Gelman, Andrew, Robert, Christian P. and Tackett, Jennifer (2019) Abandon statistical significance. The American Statistician, 73 (1). pp. 235-245. doi:10.1080/00031305.2018.1527253

Martin, Gael M., McCabe, Brendan, Frazier, David T., Maneesoonthorn, Worapree and Robert, Christian P. (2019) Auxiliary likelihood-based approximate Bayesian computation in state space models. Journal of Computational and Graphical Statistics, 28 (3). pp. 508-522. doi:10.1080/10618600.2018.1552154

2018

Frazier, David T., Martin, Gael M., Robert, Christian P. and Rousseau, Judith (2018) Asymptotic properties of approximate Bayesian computation. Biometrika, 105 (3). pp. 593-607. doi:10.1093/biomet/asy027

Robert, Christian P., Elvira, Victor, Tawn, Nicholas and Wu, Changye (2018) Accelerating MCMC algorithms. Wiley Interdisciplinary Reviews: Computational Statistics, 10 (5). e1435. doi:10.1002/wics.1435

Robert, Christian P. (2018) Beyond the Bayes factor, a new Bayesian paradigm for handling hypothesis testing. In: Chattopadhyay, Asis Kumar and Chattopadhyay, Gaurangadeb, (eds.) Statistics and its Applications. PJICAS 2016. Springer Proceedings in Mathematics & Statistics, 244 . Singapore: Springer Nature Singapore Pte Ltd., pp. 133-137. ISBN 978981131223612

Marin, Jean-Michel, Pudlo, Pierre, Estoup, Arnaud and Robert, Christian P. (2018) Likelihood-free model choice. In: Sisson, Scott A. and Fan, Yanan and Beaumont, Mark, (eds.) Handbook of Approximate Bayesian Computation. Handbooks of Modern Statistical Methods . Boca Raton, Florida: Chapman and Hall/CRC , pp. 1-21. ISBN 9781439881507

Kamary, Kaniav, Lee, Jeong Eun and Robert, Christian P. (2018) Weakly informative reparameterizations for location-scale mixtures. Journal of Computational and Graphical Statistics, 27 (4). pp. 836-848. doi:10.1080/10618600.2018.1438900

Grazian, Clare and Robert, Christian P. (2018) Jeffreys priors for mixture estimation : properties and alternatives. Computational Statistics & Data Analysis, 121 . pp. 149-163. doi:10.1016/j.csda.2017.12.005

Kamary, Kaniav, Lee, Jeong Eun and Robert, Christian P. (2018) Weakly informative reparameterisations for location-scale mixtures. Journal of Computational and Graphical Statistics, 27 (4). pp. 836-848. doi:10.1080/10618600.2018.1438900

2016

Pudlo, Pierre, Marin, Jean-Michel, Estoup, Arnaud, Cornuet, Jean-Marie, Gautier, Mathieu and Robert, Christian P. (2016) Reliable ABC model choice via random forests. Bioinformatics, 32 (6). pp. 859-866. doi:10.1093/bioinformatics/btv684

2015

Green, Peter J., Łatuszyński, Krzysztof, Pereyra, Marcelo and Robert, Christian P. (2015) Bayesian computation : a summary of the current state, and samples backwards and forwards. Statistics and Computing, 25 (4). pp. 835-862. doi:10.1007/s11222-015-9574-5

Moores, Matthew T., Drovandi, Christopher C., Mengersen, Kerrie and Robert, Christian P. (2015) Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing, 25 (1). pp. 23-33. doi:10.1007/s11222-014-9525-6

2014

Robert, Christian P. (2014) On the Jeffreys-Lindley paradox. Philosophy of Science, 81 (2). pp. 216-232. doi:10.1086/675729

Marin, Jean-Michel, Pillai, Natesh S., Robert, Christian P. and Rousseau, Judith (2014) Relevant statistics for Bayesian model choice. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76 (5). pp. 833-859. doi:10.1111/rssb.12056

2013

Mengersen, K. L., Pudlo, P. and Robert, Christian P. (2013) Bayesian computation via empirical likelihood. Proceedings of the National Academy of Sciences of the United States of America, Volume 110 (Number 4). pp. 1321-1326. doi:10.1073/pnas.1208827110

2007

Kendall, W. S., Marin, Jean-Michel and Robert, Christian P. (2007) Confidence bands for Brownian motion and applications to Monte Carlo simulation. Statistics and Computing, Vol.17 (No.1). pp. 1-10. doi:10.1007/s11222-006-9001-z

2005

Kendall, W. S., Marin, Jean-Michel and Robert, Christian P. (2005) Brownian confidence bands on Monte Carlo output. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2005 (No.2).