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

Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. (2020) “Unbiased Markov chain Monte Carlo for intractable target distributions”, Electronic Journal of Statistics, 14(2), pp. 2842–2891.
Finke, A., Doucet, A. and Johansen, A. (2020) “Limit theorems for sequential MCMC methods”, Advances in Applied Probability, 52(2), pp. 377–403.
Schmon, S., Deligiannidis, G., Doucet, A. and Pitt, M. (2020) “Large sample asymptotics of the pseudo-marginal method”, Biometrika, 108(1), pp. 37–51.
Caterini, A., Cornish, R., Sejdinovic, D. and Doucet, A. (2020) “Variational Inference with Continuously-Indexed Normalizing Flows”, in.
Tadic, V. and Doucet, A. (2020) “Stability of optimal filter higher-order derivatives”, in 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, pp. 1644–1649.
Tadic, V. and Doucet, A. (2020) “Stability of optimal filter higher-order derivatives”, Stochastic Processes and their Applications, 130(8), pp. 4808–4858.
Cornish, R., Caterini, A., Deligiannidis, G. and Doucet, A. (2020) “Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows”, in INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119.
Chen, Y., Friesen, A., Behbahani, F., Doucet, A., Budden, D., Hoffman, M. and de Freitas, N. (2020) “Modular meta-learning with shrinkage”, in Advances in Neural Information Processing Systems.
Teh, Y., Dupont, E. and Doucet, A. (2019) “Augmented neural ODEs”, Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS 2019), 32(2019), pp. 1–11.
Tadic, V. and Doucet, A. (2019) “Asymptotic properties of recursive particle maximum likelihood estimation”, in. IEEE, pp. 887–891.

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