Christian P. Robert (born 1961) is one of the most prolific and influential figures in modern Bayesian computation. A professor at Université Paris-Dauphine and a visiting researcher at the University of Warwick, he has made fundamental contributions to Monte Carlo methods, Bayesian model choice, approximate Bayesian computation, and the theoretical foundations of Bayesian inference. His textbooks, including The Bayesian Choice and Monte Carlo Statistical Methods (with George Casella), have educated a generation of Bayesian practitioners and are among the most widely cited references in computational statistics.
Education and Career
Robert studied at École Normale Supérieure in Paris and received his doctorate from the University of Rouen. He joined Université Paris-Dauphine, where he has spent most of his career, while also maintaining long-term research collaborations at CREST (Centre de Recherche en Économie et Statistique) and the University of Warwick. He has been a central figure in the French and international Bayesian communities.
Monte Carlo Statistical Methods
Robert's textbook Monte Carlo Statistical Methods, co-authored with George Casella and first published in 1999 (second edition 2004), is one of the definitive references on computational methods for Bayesian statistics. It covers Monte Carlo integration, importance sampling, MCMC methods (Metropolis-Hastings, Gibbs sampling), convergence diagnostics, and advanced topics such as perfect sampling and population Monte Carlo. The book is notable for combining rigorous mathematical treatment with practical computational guidance.
Robert's The Bayesian Choice (first edition 1994, second edition 2007) provides a comprehensive and mathematically rigorous introduction to Bayesian statistics from a decision-theoretic perspective. It covers the foundations of Bayesian inference, prior construction, Bayesian estimation and testing, model comparison, and computation. The book is distinguished by its careful attention to mathematical detail and its integration of theoretical and computational perspectives.
Approximate Bayesian Computation
Robert has been a leading developer of Approximate Bayesian Computation (ABC) methods, which enable Bayesian inference for models where the likelihood function is intractable or computationally prohibitive. ABC methods bypass the likelihood by simulating data from the model and comparing simulated and observed data through summary statistics. Robert's work on ABC model choice, ABC convergence theory, and the calibration of ABC posteriors has been highly influential in population genetics, ecology, and systems biology.
“Monte Carlo methods constitute the single most important revolution in statistics since the introduction of the computer. They have made Bayesian inference practical for a vast range of problems that were previously intractable.”— Christian P. Robert
Bayesian Model Choice
Robert has made important contributions to the theory and practice of Bayesian model choice, including work on Bayes factors, intrinsic priors for model comparison, and the Savage-Dickey density ratio for computing Bayes factors from MCMC output. He has also worked on the consistency of Bayesian model selection procedures and on the interpretation of Bayes factors, contributing to ongoing debates about the proper role of model comparison in Bayesian statistics.
Blogging and Community Building
Robert is unusual among leading statisticians for his active engagement with the broader community through his blog, Xi'an's Og, where he reviews papers, discusses statistical methodology, and comments on current developments in Bayesian statistics. This sustained public engagement has made him an important voice in contemporary statistical discourse, connecting researchers across institutions and disciplines.
Recognition
Robert is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and has received numerous awards. He served as editor of the Journal of the Royal Statistical Society, Series B, one of the most prestigious journals in statistics, and has been active in the leadership of the International Society for Bayesian Analysis.
Born in France.
Received doctorate from the University of Rouen.
Published the first edition of The Bayesian Choice.
Published Monte Carlo Statistical Methods with George Casella.
Published major work on ABC model choice and theory.
Served as editor of JRSS Series B.