Nicolas Chopin

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Omiros PapaspiliopoulosBocconiVerified email at unibocconi.it
Sara MartinoNorwegian University of Science and TechnologyVerified email at ntnu.no
Håvard RueKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr
Sumeetpal Sidhu SinghProfessor of Engineering Statistics and Head of the Signal Processing and Communications GroupVerified email at cam.ac.uk
Simon BarthelméCNRS Research Fellow, Gipsa-labVerified email at gipsa-lab.fr
Pierre AlquierCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp
Pierre E. JacobESSEC Business SchoolVerified email at essec.edu
Judith RousseauUniversity of oxfordVerified email at stats.ox.ac.uk
James RidgwayCapital Fund ManagementVerified email at cfm.fr
Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, Google DeepMindVerified email at stats.ox.ac.uk
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityVerified email at stat.columbia.edu
Christophe AndrieuProfessor of Statistics, University of BristolVerified email at bristol.ac.uk
Nikolas KantasDept. of Mathematics, Imperial College LondonVerified email at imperial.ac.uk
Jan MaciejowskiUniversity of CambridgeVerified email at eng.cam.ac.uk
Robin J. RyderCEREMADE, Université Paris DauphineVerified email at ceremade.dauphine.fr
Nick WhiteleyUniversity of BristolVerified email at bristol.ac.uk
LELIEVRE, TONYProfessor of Applied Mathematics, Ecole des Ponts ParisTech and INRIAVerified email at cermics.enpc.fr

Nicolas Chopin

Professor of Statistics, ENSAE (Institut Polytechnique de Paris)

Verified email at ensae.fr - Homepage

Statistics Bayesian Stastistics Bayesian Machine Learning Monte Carlo methods Bayesian computation particle filtering


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Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations H Rue, S Martino, N Chopin Journal of the royal statistical society: Series b (statistical methodology …, 2009	4310	2009
A sequential particle filter method for static models N Chopin Biometrika 89 (3), 539-552, 2002	721	2002
Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference N Chopin The Annals of Statistics 32 (6), 2385-2411, 2004	494	2004
On particle methods for parameter estimation in state-space models N Kantas, A Doucet, SS Singh, J Maciejowski, N Chopin Statistical science 30 (3), 328-351, 2015	430	2015
SMC²: an efficient algorithm for sequential analysis of state space models N Chopin, PE Jacob, O Papaspiliopoulos Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2013	413*	2013
Theory of Probability revisited (with discussion) C Robert, N Chopin, J Rousseau Statist. Science 24 (2), 141-172, 2009	194*	2009
Control functionals for Monte Carlo integration CJ Oates, M Girolami, N Chopin Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2017	190	2017
On the properties of variational approximations of Gibbs posteriors P Alquier, J Ridgway, N Chopin The Journal of Machine Learning Research 17 (1), 8374-8414, 2016	145	2016
Fast simulation of truncated Gaussian distributions N Chopin Statistics and Computing 21 (2), 275-288, 2011	137	2011
On particle Gibbs sampling N Chopin, SS Singh Bernoulli 21 (3), 1855-1883, 2015	136	2015
Sequential quasi monte carlo M Gerber, N Chopin Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2015	136	2015
Properties of nested sampling N Chopin, CP Robert Biometrika 97 (3), 741-755, 2010	115	2010
Expectation propagation for likelihood-free inference S Barthelmé, N Chopin Journal of the American Statistical Association 109 (505), 315-333, 2014	111	2014
Sequential Monte Carlo on large binary sampling spaces C Schäfer, N Chopin Statistics and Computing 23 (2), 163-184, 2013	101	2013
An introduction to sequential Monte Carlo N Chopin, O Papaspiliopoulos Springer International Publishing, 2020	97	2020
Dynamic detection of change points in long time series N Chopin Annals of the Institute of Statistical Mathematics 59 (2), 349-366, 2007	76	2007
Expectation propagation as a way of life A Gelman, A Vehtari, P Jylänki, C Robert, N Chopin, JP Cunningham arXiv preprint arXiv:1412.4869 157, 2014	70	2014
Negative association, ordering and convergence of resampling methods M Gerber, N Chopin, N Whiteley The Annals of Statistics 47 (4), 2236-2260, 2019	59	2019
Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation N Chopin, J Ridgway Statistical Science 32 (1), 64-87, 2017	54	2017
Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation N Chopin, F Pelgrin Journal of Econometrics 123 (2), 327-344, 2004	54	2004

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