| 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 | 6791 | 2009 |
| A sequential particle filter method for static models N Chopin Biometrika 89 (3), 539-552, 2002 | 922 | 2002 |
| On particle methods for parameter estimation in state-space models N Kantas, A Doucet, SS Singh, J Maciejowski, N Chopin | 653 | 2015 |
| Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference N Chopin | 607 | 2004 |
| An introduction to sequential Monte Carlo N Chopin, O Papaspiliopoulos Springer, 2020 | 598 | 2020 |
| 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 | 581 | 2013 |
| 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 | 328 | 2016 |
| Control functionals for Monte Carlo integration CJ Oates, M Girolami, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 287 | 2017 |
| Theory of Probability revisited (with discussion) C Robert, N Chopin, J Rousseau Statist. Science 24 (2), 141-172, 2009 | 268* | 2009 |
| On particle Gibbs sampling N Chopin, SS Singh | 195 | 2015 |
| A Bayesian information criterion for singular models M Drton, M Plummer Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 194 | 2017 |
| Sequential quasi monte carlo M Gerber, N Chopin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015 | 171 | 2015 |
| Fast simulation of truncated Gaussian distributions N Chopin Statistics and Computing 21 (2), 275-288, 2011 | 170 | 2011 |
| Properties of nested sampling N Chopin, CP Robert Biometrika 97 (3), 741-755, 2010 | 150 | 2010 |
| Sequential Monte Carlo on large binary sampling spaces C Schäfer, N Chopin Statistics and Computing 23 (2), 163-184, 2013 | 129 | 2013 |
| Expectation propagation for likelihood-free inference S Barthelmé, N Chopin Journal of the American Statistical Association 109 (505), 315-333, 2014 | 125 | 2014 |
| Negative association, ordering and convergence of resampling methods M Gerber, N Chopin, N Whiteley The Annals of Statistics 47 (4), 2236-2260, 2019 | 102 | 2019 |
| Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation N Chopin, J Ridgway | 102 | 2017 |
| Dynamic detection of change points in long time series N Chopin Annals of the Institute of Statistical Mathematics 59 (2), 349-366, 2007 | 89 | 2007 |
| Adaptive tuning of hamiltonian monte carlo within sequential monte carlo A Buchholz, N Chopin, PE Jacob Bayesian Analysis 16 (3), 745-771, 2021 | 66 | 2021 |
Omiros PapaspiliopoulosBocconiVerified email at unibocconi.it
