Arnaud Doucet

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Co-authors

Christophe AndrieuProfessor of Statistics, University of BristolVerified email at bristol.ac.uk
Nando de FreitasCIFAR & DeepMindVerified email at google.com
Sumeetpal Sidhu SinghReader in Engineering Statistics, Department of Engineering, Cambridge UniversityVerified email at cam.ac.uk
Pierre Del MoralResearch Director, INRIA Bordeaux - Sud OuestVerified email at inria.fr
Adam M. JohansenUniversity of WarwickVerified email at warwick.ac.uk
Simon GodsillUniversity of CambridgeVerified email at eng.cam.ac.uk
Ajay JasraProfessor of Applied Mathematics, KAUSTVerified email at kaust.edu.sa
Ba-Ngu VoCurtin UniversityVerified email at curtin.edu.au
Neil GordonDefence Science and Technology GroupVerified email at uq.edu.au
George DeligiannidisAssociate Professor in Statistics, University of OxfordVerified email at stats.ox.ac.uk
François CaronUniversity of OxfordVerified email at stats.ox.ac.uk
Chris HolmesVerified email at stats.ox.ac.uk
Mike WestDuke UniversityVerified email at nc.rr.com
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Anthony LeeSchool of Mathematics, University of BristolVerified email at bristol.ac.uk
M PittKing's College LondonVerified email at kcl.ac.uk
Dan CrisanProfessor of Mathematics, Imperial College LondonVerified email at imperial.ac.uk
Marco CuturiGoogle Brain / CREST-ENSAE.Verified email at google.com
Soufiane HayouDepartment of Statistics, University of OxfordVerified email at stats.ox.ac.uk

Arnaud Doucet

Professor of Statistics, University of Oxford & Research Scientist, Google DeepMind

Verified email at stats.ox.ac.uk - Homepage

Bayesian Inference Computational Statistics Machine Learning Monte Carlo methods


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Sequential Monte Carlo Methods in Practice A Doucet, N De Freitas, N Gordon Sequential monte carlo methods in practice, 2001	9353*	2001
On sequential Monte Carlo sampling methods for Bayesian filtering A Doucet, S Godsill, C Andrieu Statistics and computing 10 (3), 197-208, 2000	5767	2000
An introduction to MCMC for machine learning C Andrieu, N De Freitas, A Doucet, MI Jordan Machine learning 50 (1), 5-43, 2003	2723	2003
The unscented particle filter R Van Der Merwe, A Doucet, N De Freitas, EA Wan	2375	2000
A tutorial on particle filtering and smoothing: Fifteen years later A Doucet, AM Johansen Oxford Handbook of Nonlinear Filtering 12 (656-704), 3, 2011	2095	2011
Particle Markov chain Monte Carlo methods C Andrieu, A Doucet, R Holenstein Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2010	1947	2010
Rao-Blackwellised particle filtering for dynamic Bayesian networks A Doucet, N De Freitas, K Murphy, S Russell UAI 2000, 2000	1765*	2000
Sequential Monte Carlo samplers P Del Moral, A Doucet, A Jasra Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2006	1552	2006
Sequential Monte Carlo methods for multitarget filtering with random finite sets BN Vo, S Singh, A Doucet IEEE Transactions on Aerospace and electronic systems 41 (4), 1224-1245, 2005	1402	2005
An introduction to sequential Monte Carlo methods A Doucet, N De Freitas, N Gordon Sequential Monte Carlo methods in practice, 3-14, 2001	1237	2001
A survey of convergence results on particle filtering methods for practitioners D Crisan, A Doucet IEEE Transactions on signal processing 50 (3), 736-746, 2002	1177	2002
On sequential simulation-based methods for Bayesian filtering A Doucet Technical Report CUED/F-INFENG/TR.310 Cambridge University, 1998	1011	1998
Particle filters for state estimation of jump Markov linear systems A Doucet, NJ Gordon, V Krishnamurthy IEEE Transactions on signal processing 49 (3), 613-624, 2001	1005	2001
Monte Carlo smoothing for nonlinear time series SJ Godsill, A Doucet, M West Journal of the american statistical association, 2004	588	2004
Maintaining multi-modality through mixture tracking J Vermaak, A Doucet, P Patrick International Conference on Computer Vision, 1110, 2003	544	2003
An adaptive sequential Monte Carlo method for approximate Bayesian computation P Del Moral, A Doucet, A Jasra Statistics and computing 22 (5), 1009-1020, 2012	433	2012
Fast computation of Wasserstein barycenters M Cuturi, A Doucet Proceedings of the 31st International Conference on Machine Learning, 685-693, 2014	420	2014
Sequential Monte Carlo implementation of the PHD filter for multi-target tracking BN Vo, S Singh, A Doucet Proc. Int’l Conf. on Information Fusion, 792-799, 2003	420	2003
Particle methods for change detection, system identification, and control C Andrieu, A Doucet, SS Singh, VB Tadic Proceedings of the IEEE 92 (3), 423-438, 2004	382	2004
Smoothing algorithms for state–space models M Briers, A Doucet, S Maskell Annals of the Institute of Statistical Mathematics 62 (1), 61, 2010	337	2010

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