| An entropy criterion for assessing the number of clusters in a mixture model G Celeux, G Soromenho Journal of classification 13, 195-212, 1996 | 2736 | 1996 |
| Assessing a mixture model for clustering with the integrated completed likelihood C Biernacki, G Celeux, G Govaert IEEE transactions on pattern analysis and machine intelligence 22 (7), 719-725, 2002 | 2062 | 2002 |
| Gaussian parsimonious clustering models G Celeux, G Govaert Pattern recognition 28 (5), 781-793, 1995 | 1304 | 1995 |
| A classification EM algorithm for clustering and two stochastic versions G Celeux, G Govaert Computational statistics & Data analysis 14 (3), 315-332, 1992 | 1182 | 1992 |
| Deviance information criteria for missing data models G Celeux, F Forbes, CP Robert, DM Titterington | 1126 | 2006 |
| The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem G Celeux Computational statistics quarterly 2, 73-82, 1985 | 893 | 1985 |
| Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models C Biernacki, G Celeux, G Govaert Computational Statistics & Data Analysis 41 (3-4), 561-575, 2003 | 855 | 2003 |
| Computational and inferential difficulties with mixture posterior distributions G Celeux, M Hurn, CP Robert Journal of the American Statistical Association 95 (451), 957-970, 2000 | 836 | 2000 |
| Stochastic versions of the EM algorithm: an experimental study in the mixture case G Celeux, D Chauveau, J Diebolt Journal of statistical computation and simulation 55 (4), 287-314, 1996 | 477 | 1996 |
| EM procedures using mean field-like approximations for Markov model-based image segmentation G Celeux, F Forbes, N Peyrard Pattern recognition 36 (1), 131-144, 2003 | 444 | 2003 |
| Combining mixture components for clustering JP Baudry, AE Raftery, G Celeux, K Lo, R Gottardo Journal of computational and graphical statistics 19 (2), 332-353, 2010 | 404 | 2010 |
| Model-based clustering and classification for data science: with applications in R C Bouveyron, G Celeux, TB Murphy, AE Raftery Cambridge University Press, 2019 | 352 | 2019 |
| Classification automatique des données G Celeux, E Diday, G Govaert, Y Lechevallier, H Ralambondrainy Dunod, 1989 | 333 | 1989 |
| Variable selection for clustering with Gaussian mixture models C Maugis, G Celeux, ML Martin-Magniette Biometrics 65 (3), 701-709, 2009 | 315 | 2009 |
| Inference in model-based cluster analysis H Bensmail, G Celeux, AE Raftery, CP Robert statistics and Computing 7, 1-10, 1997 | 305 | 1997 |
| Regularized Gaussian discriminant analysis through eigenvalue decomposition H Bensmail, G Celeux Journal of the American statistical Association 91 (436), 1743-1748, 1996 | 261 | 1996 |
| Data-based filtering for replicated high-throughput transcriptome sequencing experiments A Rau, M Gallopin, G Celeux, F Jaffrézic Bioinformatics 29 (17), 2146-2152, 2013 | 254 | 2013 |
| Bayesian estimation of hidden Markov chains: a stochastic implementation CP Robert, G Celeux, J Diebolt Statistics & Probability Letters 16 (1), 77-83, 1993 | 245 | 1993 |
| Model-based cluster and discriminant analysis with the MIXMOD software C Biernacki, G Celeux, G Govaert, F Langrognet Computational Statistics & Data Analysis 51 (2), 587-600, 2006 | 224 | 2006 |
| A stochastic approximation type EM algorithm for the mixture problem G Celeux, J Diebolt Stochastics: An International Journal of Probability and Stochastic …, 1992 | 216 | 1992 |
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