Prof. Arnaud Doucet

Positions: Professor.
Departments: Department of Statistics.

Prof. Arnaud Doucet’s research concerns numerical methods for the analysis of complex data sets. In particular, he has contributed to the development and study of sequential Monte Carlo and Markov chain Monte Carlo methods.

Selected Publications

Doucet, A., Pitt, M.K., Deligiannidis, G. and John, R. (2015) ‘Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator’, Biometrika, 102(2), pp. 295–313.

Lienert, T.., Whye Teh, Y.., and Doucet, (2015) ‘Expectation Particle Belief Propagation’, Neural Information Processing Systems.

Paige, B., Wood, F., Doucet, A. and Teh, Y.W. (2014) ‘Asynchronous anytime Sequential Monte Carlo’, Neural Information Processing Systems (2014).

Lee, A., Caron, F., Doucet, A. and Holmes, C. (2012) ‘Bayesian Sparsity-Path-Analysis of genetic association signal using generalized t Priors’, Statistical Applications in Genetics and Molecular Biology, 11(2).

Poyiadjis, G., Doucet, A. and Singh, S.S. (2011) ‘Particle approximations of the score and observed information matrix in state space models with application to parameter estimation’,Biometrika, 98(1), pp. 65–80.

Andrieu, C., Doucet, A. and Holenstein, R. (2010) ‘Particle Markov chain Monte Carlo methods’,Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3), pp. 269–342.

Del Moral, P., Doucet, A. and Jasra, A. (2006) ‘Sequential Monte Carlo samplers’, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(3), pp. 411–436.

Contact me

doucet@stats.ox.ac.uk

Website