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 SinghTibra Foundation Chair in Mathematical Sciences, School of Mathematics, University of WollongongVerified email at uow.edu.au
Pierre AlquierESSEC Business SchoolVerified email at essec.edu
Pierre E. JacobESSEC Business SchoolVerified email at essec.edu
Simon BarthelméCNRS Research Fellow, Gipsa-labVerified email at gipsa-lab.fr
James RidgwayCapital Fund ManagementVerified email at cfm.fr
Judith RousseauUniversity of oxfordVerified email at stats.ox.ac.uk
Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, Google DeepMindVerified email at google.com
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Christophe AndrieuProfessor of Statistics, University of BristolVerified email at bristol.ac.uk
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Nikolas KantasDept. of Mathematics, Imperial College LondonVerified email at imperial.ac.uk
Jan MaciejowskiUniversity of CambridgeVerified email at cam.ac.uk
Nick WhiteleyUniversity of BristolVerified email at bristol.ac.uk
Alexander BuchholzAmazonVerified email at amazon.de
Robin J. RyderImperial College LondonVerified email at imperial.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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	5791	2009
A sequential particle filter method for static models N Chopin Biometrika 89 (3), 539-552, 2002	841	2002
Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference N Chopin	573	2004
On particle methods for parameter estimation in state-space models N Kantas, A Doucet, SS Singh, J Maciejowski, N Chopin	559	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	514	2013
An introduction to sequential Monte Carlo N Chopin, O Papaspiliopoulos Springer, 2020	371	2020
On the properties of variational approximations of Gibbs posteriors P Alquier, J Ridgway, N Chopin Journal of Machine Learning Research 17 (236), 1-41, 2016	259	2016
Control functionals for Monte Carlo integration CJ Oates, M Girolami, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017	250	2017
Theory of Probability revisited (with discussion) C Robert, N Chopin, J Rousseau Statist. Science 24 (2), 141-172, 2009	239*	2009
On particle Gibbs sampling N Chopin, SS Singh	179	2015
Fast simulation of truncated Gaussian distributions N Chopin Statistics and Computing 21, 275-288, 2011	161	2011
Sequential quasi monte carlo M Gerber, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015	160	2015
A Bayesian information criterion for singular models M Drton, M Plummer Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017	153	2017
Properties of nested sampling N Chopin, CP Robert Biometrika 97 (3), 741-755, 2010	138	2010
Expectation propagation for likelihood-free inference S Barthelmé, N Chopin Journal of the American Statistical Association 109 (505), 315-333, 2014	120	2014
Sequential Monte Carlo on large binary sampling spaces C Schäfer, N Chopin Statistics and Computing 23, 163-184, 2013	118	2013
Negative association, ordering and convergence of resampling methods M Gerber, N Chopin, N Whiteley The Annals of Statistics 47 (4), 2236-2260, 2019	92	2019
Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation N Chopin, J Ridgway	88	2017
Dynamic detection of change points in long time series N Chopin Annals of the Institute of Statistical Mathematics 59 (2), 349-366, 2007	86	2007
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	55	2004

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