Professor Arnaud Doucet

Professor of Statistics

Biographical Sketch

I obtained my PhD from the University of Paris XI (Orsay) in 1997. I previously held academic positions at Cambridge University, Melbourne University, The Institute of Statistical Mathematics in Tokyo and the University of British Columbia where I was a Canada Research Chair in Stochastic Computation.

Research Interests

  • Bayesian statistics
  • Stochastic simulations
  • Sequential Monte Carlo
  • Markov chain Monte Carlo
  • Time series

I am primarily interested in the development and study of novel Monte Carlo methods for inference in complex stochastic models.

Publications

Heng, J., Doucet, A. and Pokern, Y. (2021) “Gibbs Flow for Approximate Transport with Applications to Bayesian Computation”, Journal of the Royal Statistical Society Series B (Statistical Methodology). Oxford University Press (OUP).
Heng, J., Doucet, A. and Pokern, Y. (2021) “Gibbs flow for approximate transport with applications to Bayesian computation”, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83(1), pp. 156–187.
Ruiz, F., Titsias, M., Cemgil, T. and Doucet, A. (2021) “Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains”, in 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, pp. 707–717.
Caterini, A., Cornish, R., Sejdinovic, D. and Doucet, A. (2021) “Variational Inference with Continuously-Indexed Normalizing Flows”, in 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, pp. 44–53.
Corenflos, A., Thornton, J., Deligiannidis, G. and Doucet, A. (2021) “Differentiable Particle Filtering via Entropy-Regularized Optimal Transport”, in INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139.
Tadic, V. and Doucet, A. (2020) “Asymptotic properties of recursive particle maximum likelihood estimation”, IEEE Transactions on Information Theory, 67(3), pp. 1825–1848.
Deligiannidis, G., Paulin, D., Bouchard-Côté, A. and Doucet, A. (2020) “Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates.”
Deligiannidis, G., Paulin, D., Bouchard-Côté, A. and Doucet, A. (2020) “Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates”. University of Oxford.
Gagnon, P. and Doucet, A. (2020) “Nonreversible jump algorithms for Bayesian nested model selection”, Journal of Computational and Graphical Statistics, 30(2), pp. 312–323.
Heng, J., Bishop, A., Deligiannidis, G. and Doucet, A. (2020) “Controlled sequential Monte Carlo”, Annals of Statistics, 48(5), pp. 2904–2929.

Contact Details

College affiliation: Fellow at Hertford College

Email:  doucet@stats.ox.ac.uk

Telephone: +44(0)1865 285368

Office number: 1.09

PA:  Lauren Haynes

Graduate Students

Joe Benton

Andrew Campbell

Amitis Shidani

Eugenio Clerico

Guneet Singh Dhillon

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology