Nicolas Chopin

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Omiros PapaspiliopoulosBocconiCorreu electrònic verificat a unibocconi.it
Sara MartinoNorwegian University of Science and TechnologyCorreu electrònic verificat a ntnu.no
Håvard RueKing Abdullah University of Science and TechnologyCorreu electrònic verificat a kaust.edu.sa
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickCorreu electrònic verificat a ceremade.dauphine.fr
Sumeetpal Sidhu SinghTibra Foundation Chair in Mathematical Sciences, School of Mathematics, University of WollongongCorreu electrònic verificat a uow.edu.au
Pierre AlquierESSEC Business SchoolCorreu electrònic verificat a essec.edu
Pierre E. JacobESSEC Business SchoolCorreu electrònic verificat a essec.edu
Simon BarthelméCNRS Research Fellow, Gipsa-labCorreu electrònic verificat a gipsa-lab.fr
James RidgwayCapital Fund ManagementCorreu electrònic verificat a cfm.fr
Judith RousseauUniversity of oxfordCorreu electrònic verificat a stats.ox.ac.uk
Arnaud DoucetGoogle DeepMindCorreu electrònic verificat a google.com
Mark GirolamiUniversity of CambridgeCorreu electrònic verificat a cam.ac.uk
Christophe AndrieuProfessor of Statistics, University of BristolCorreu electrònic verificat a bristol.ac.uk
Chris. J. OatesNewcastle UniversityCorreu electrònic verificat a ncl.ac.uk
Nikolas KantasDept. of Mathematics, Imperial College LondonCorreu electrònic verificat a imperial.ac.uk
Jan MaciejowskiUniversity of CambridgeCorreu electrònic verificat a cam.ac.uk
Nick WhiteleyUniversity of BristolCorreu electrònic verificat a bristol.ac.uk
Robin J. RyderImperial College LondonCorreu electrònic verificat a imperial.ac.uk
Alexander BuchholzAmazonCorreu electrònic verificat a amazon.de
LELIEVRE, TONYProfessor of Applied Mathematics, Ecole des Ponts, Institut Polytechnique de Paris, and INRIACorreu electrònic verificat a cermics.enpc.fr

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Nicolas Chopin

Professor of Statistics, ENSAE (Institut Polytechnique de Paris)

Correu electrònic verificat a ensae.fr - Pàgina d'inici

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


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
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	6035	2009
A sequential particle filter method for static models N Chopin Biometrika 89 (3), 539-552, 2002	865	2002
On particle methods for parameter estimation in state-space models N Kantas, A Doucet, SS Singh, J Maciejowski, N Chopin	584	2015
Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference N Chopin	583	2004
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	529	2013
An introduction to sequential Monte Carlo N Chopin, O Papaspiliopoulos Springer, 2020	428	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	279	2016
Control functionals for Monte Carlo integration CJ Oates, M Girolami, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017	261	2017
Theory of Probability revisited (with discussion) C Robert, N Chopin, J Rousseau Statist. Science 24 (2), 141-172, 2009	250*	2009
On particle Gibbs sampling N Chopin, SS Singh	184	2015
Sequential quasi monte carlo M Gerber, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015	164	2015
A Bayesian information criterion for singular models M Drton, M Plummer Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017	163	2017
Fast simulation of truncated Gaussian distributions N Chopin Statistics and Computing 21, 275-288, 2011	162	2011
Properties of nested sampling N Chopin, CP Robert Biometrika 97 (3), 741-755, 2010	139	2010
Sequential Monte Carlo on large binary sampling spaces C Schäfer, N Chopin Statistics and Computing 23, 163-184, 2013	121	2013
Expectation propagation for likelihood-free inference S Barthelmé, N Chopin Journal of the American Statistical Association 109 (505), 315-333, 2014	120	2014
Negative association, ordering and convergence of resampling methods M Gerber, N Chopin, N Whiteley The Annals of Statistics 47 (4), 2236-2260, 2019	94	2019
Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation N Chopin, J Ridgway	94	2017
Dynamic detection of change points in long time series N Chopin Annals of the Institute of Statistical Mathematics 59 (2), 349-366, 2007	87	2007
Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors N Chopin, T Lelièvre, G Stoltz Statistics and Computing 22, 897-916, 2012	55*	2012

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