Professor Judith Rousseau

Professor of Statistics

Biographical Sketch

Judith is an associate editor of the Annals of Statistics, Bernoulli, ANZJS and stat and is currently  the program secretary of IMS. She has also been active on various aspects of the ISBA society. She is an ISBA and an IMS fellow and has received the Ethel Newbold prize in 2015 and gave a medallion lecture in July 2017. Before coming to Oxford, she was a Professor at University Paris Dauphine.

Research Interests

  • Bayesian Statistics
    • Default Bayesian analysis
    • Nonparametric Bayesian statistics
    • Bayesian testing
  • Interaction between Bayesian and frequentist approaches
    • Frequentist properties of Bayesian methods
    • Asymptotic analysis
  • Mixture distributions
  • MCMC algorithms

Judith’s research interests range from theoretical aspects of Bayesian procedures, both parametric and nonparametric, to more methodological developments. From a theoretical perspective she is interested in the interface between Bayesian and frequentist approaches, looking at frequentist properties of Bayesian methods. From a more methodological perspective, she has worked on MCMC or related algorithms and on the elicitation of subjective priors.

Publications

Sulem, D., Rivoirard, V. and Rousseau, J. (2024) “Bayesian estimation of nonlinear Hawkes process”, Bernoulli, 30(2), pp. 1257–1286.
Caron, F., Panero, F. and Rousseau, J. (2023) “On sparsity, power-law and clustering properties of graphex processes”, Advances in Applied Probability, 55(4), pp. 1211–1253.
Robert, C. and Rousseau, J. (2023) “A special issue on Bayesian inference: challenges, perspectives and prospects.”, Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 381(2247), p. 20220155.
Naik, C., Rousseau, J. and Campbell, T. (2023) “Fast Bayesian coresets via subsampling and quasi-Newton refinement”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 70–83.
Naik, C., Caron, F., Rousseau, J., Teh, Y. and Palla, K. (2023) “Bayesian nonparametrics for sparse dynamic networks”, in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Springer Nature, pp. 191–206.
Naik, C., Caron, F., Rousseau, J., Teh, Y. and Palla, K. (2023) “Bayesian Nonparametrics for Sparse Dynamic Networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature, pp. 191–206.
Rockova, V. and Rousseau, J. (2023) “Ideal Bayesian Spatial Adaptation”, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION [Preprint].
Naik, C., Rousseau, J. and Campbell, T. (2022) “Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement”, Advances in Neural Information Processing Systems, 35.
Hayou, S., Clerico, E., He, B., Deligiannidis, G., Doucet, A. and Rousseau, J. (2021) “Stable ResNet”, in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Journal of Machine Learning Research, pp. 1324–1332.
Naik, C., Caron, F. and Rousseau, J. (2021) “Sparse networks with core-periphery structure”, Electronic Journal of Statistics, 15(1), pp. 1814–1868.

Contact Details

Email: judith.rousseau@stats.ox.ac.uk

Office: 1.09

Bayesian Reading Group

Graduate Students

Paul Rosa

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology