, Prévision de temps d'échange lors des stationnements de trains en gare

, Université Paris Sud), co-direction d'une thèse Cifre SNCF débutant en octobre, 2019.

, Un des minorants du temps de stationnement est le temps d'échange, déterminé par le temps mis pour faire descendre et monter tous les passagers qui le souhaitent. Il dépend, entre autres, du train, de sa mission, du calendrier, des horaires, de la charge instantanée et de la configuration du quai ou de la gare, Des études préliminaires internes à la SNCF ont montré que le problème était complexe, 2018.

. À-partir-d'un-jeu-de-données-concernant-la-ligne-e-du-transilien, nous adresserons la prédiction (apprentissage automatique) et la modélisation (statistique) : (1) construire une typologie propre gare-heure, gare-heure-type de train, par exemple avec des techniques de co-clustering ; (2) étudier les corrélations entre nombre de voyageurs (charge) et flux en gare, flux et temps de stationnement

, sein d'une même gare, ou d'un même train) comme un processus stochastique ; (4) développer un simulateur numérique réaliste des flux de voyageurs et tester différents scenarii d'incidents et de résolution

, Construction d'un critère probabilisé de fatigue multiaxiale

, Direction d'une thèse Cifre PSA débutant en novembre 2019, co-direction Patrick Pamphile (LMO

, Ceci s'applique également aux études de fiabilité de certains composants du châssis d'un véhicule, et la volonté est de réduire drastiquement le nombre d'essais physiques pour tendre vers une conception presque entièrement numérique n'ayant qu'une seule phase de validation. Les modèles déterministes, bien que développés à partir de dessins de conception détaillée, peuvent prédire des comportements différents de ceux observés sur la structure lors d'essais. Ces écarts peuvent être dus à la discrétisation plus ou moins fidèle à la géométrie de la structure, aux incertitudes sur certains paramètres du modèle (tels que les propriétés des matériaux, les conditions aux limites), ou aux chargements aléatoires subis par la structure, La digitalisation de la conception est au coeur des processus des départements métier des constructeurs automobiles, pour leur permettre de réduire les coûts et les temps de développement, 1998.

E. Allman, C. Mattias, and J. Rhodes, Identifiability of parameters in latent structure models with many observed variables, The Annals of Statistics, vol.37, pp.3099-3132, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00591202

C. Ambroise and C. Matias, New consistent and asymptotically normal parameter estimates for random-graph mixture models, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.74, issue.1, pp.3-35, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00647817

S. Arlot, Minimal penalties and the slope heuristics : a survey, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02300688

S. Arlot and A. Celisse, A survey of cross-validation procedures for model selection, Statistics surveys, vol.4, pp.40-79, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00407906

J. Aubert, Analyse statistique de données biologiques à haut débit, 2017.

A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, and D. S. Modha, A generalized maximum entropy approach to Bregman co-clustering and matrix approximation, Journal of Machine Learning Research, vol.8, pp.1919-1986, 2007.

J. Baudry, Sélection de modèle pour la classification non supervisée. Choix du nombre de classes, 2009.

M. J. Beal, Variational algorithms for approximate Bayesian inference. university of London London, 2003.

L. Beck and L. S. Katafygiotis, Updating models and their uncertainties. i : Bayesian statistical framework, Journal of Engineering Mechanics, vol.124, issue.4, pp.455-461, 1998.

L. R. Bergé, C. Bouveyron, M. Corneli, and P. Latouche, The latent topic block model for the co-clustering of textual interaction data, Computational Statistics & Data Analysis, 2019.

P. Bickel, D. Choi, X. Chang, and H. Zhang, Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels, The Annals of Statistics, vol.41, issue.4, pp.1922-1943, 2013.

C. Biernacki, Model choice and model aggregation, chapter Mixture Models, 2017.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE transactions on pattern analysis and machine intelligence, vol.22, pp.719-725, 2000.

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.561-575, 2003.

L. Birgé and P. Massart, Gaussian model selection, Journal of the European Mathematical Society, vol.3, issue.3, pp.203-268, 2001.

L. Birgé and P. Massart, Minimal penalties for Gaussian model selection, Probability theory and related fields, vol.138, pp.33-73, 2007.

C. M. Bishop, Pattern recognition and machine learning, 2006.

C. Bouveyron and C. Brunet-saumard, Model-based clustering of high-dimensional data : A review, Computational Statistics & Data Analysis, vol.71, pp.52-78, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00750909

V. Brault, Estimation et sélection de modèle pour le modèle des blocs latents, 2014.

V. Brault and A. Lomet, Revue des méthodes pour la classification jointe des lignes et des colonnes d'un tableau, Journal de la Société Française de Statistique, vol.156, issue.3, pp.27-51, 2015.

V. Brault, G. Celeux, and C. Keribin, Régularisation bayésienne du modèle des blocs latents, 44ème Journées de Statistique, 2012.

V. Brault, G. Celeux, and C. Keribin, Mise en oeuvre de l'échantillonneur de Gibbs pour le modèle des blocs latents, 46èmes journées de statistique de la SFdS, 2014.

V. Brault, C. Keribin, and M. Mariadassou, Consistency and asymptotic normality of latent block model estimators, 2019.

F. Caron and E. B. Fox, Sparse graphs using exchangeable random measures, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.79, issue.5, pp.1295-1366, 2017.

F. Caron and J. Rousseau, On sparsity and power-law properties of graphs based on exchangeable point processes, 2017.

M. K. Carroll, G. A. Cecchi, I. Rish, R. Garg, and A. R. Rao, Prediction and interpretation of distributed neural activity with sparse models, NeuroImage, vol.44, issue.1, pp.112-122, 2009.

G. Celeux and J. Diebolt, L'algorithme SEM : un algorithme d'apprentissage probabiliste pour la reconnaissance de mélange de densités, vol.34, pp.35-52, 1986.

G. Celeux and G. Govaert, A classification em algorithm for clustering and two stochastic versions, Computational statistics & Data analysis, vol.14, issue.3, pp.315-332, 1992.
URL : https://hal.archives-ouvertes.fr/inria-00075196

G. Celeux and P. Pamphile, Competing risk with masked causes and highly censored data : Non-iterative estimations, INRIA, 2019.

G. Celeux, D. Chauveau, and J. Diebolt, Stochastic versions of the EM algorithm : an experimental study in the mixture case, Journal of Statistical Computation and Simulation, vol.55, issue.4, pp.287-314, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00693519

A. Celisse, J. Daudin, and E. L. Pierre, Consistency of maximum-likelihood and variational estimators in the stochastic block model, Electronic Journal of Statistics, vol.6, pp.1847-1899, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000059

S. Chib and I. Jeliazkov, Marginal likelihood from the Metropolis-Hastings output, Journal of the American Statistical Association, vol.96, issue.453, pp.270-281, 2001.

A. Clément, A. Riviere, P. Serré, and C. Valade, The TTRSs : 13 constraints for dimensioning and tolerancing, Geometric design tolerancing : theories, standards and applications, pp.122-131, 1998.

S. Cornet, C. Buisson, F. Ramond, P. Bouvarel, and J. Rodriguez, Methods for quantitative assessment of passenger flow influence on train dwell time in dense traffic areas, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01909708

C. Coron, C. Calenge, C. Giraud, and R. Julliard, Estimation of species relative abundances and habitat preferences using opportunistic data, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01544250

D. D. Cox and R. L. Savoy, Functional magnetic resonance imaging (fMRI)"brain reading" : detecting and classifying distributed patterns of fMRI activity in human visual cortex, Neuroimage, vol.19, issue.2, pp.261-270, 2003.

D. Dacunha-castelle and E. Gassiat, Testing in locally conic models, and application to mixture models, ESAIM : Probability and Statistics, vol.1, pp.285-317, 1997.

K. Dang-van, Macro-micro approach in high-cycle multiaxial fatigue. Dans Advances in multiaxial fatigue, 1993.

J. Daudin, F. Picard, and E. S. Robin, A mixture model for random graphs, Statistics and Computing, vol.18, pp.173-183, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00070186

S. Dehaene, G. L. Clec'h, L. Cohen, J. Poline, P. Van-de-moortele et al., Inferring behavior from functional brain images, Nature neuroscience, vol.1, issue.7, p.549, 1998.
URL : https://hal.archives-ouvertes.fr/hal-00349936

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (methodological), pp.1-38, 1977.

A. Desrochers and A. Clément, A dimensioning and tolerancing assistance model for cad/cam systems, The International Journal of Advanced Manufacturing Technology, vol.9, issue.6, pp.352-361, 1994.

W. Dumouchel, Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system, The American Statistician, vol.53, issue.3, pp.177-190, 1999.

S. Evans, P. C. Waller, and E. S. Davis, Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports, Pharmacoepidemiology and drug safety, vol.10, issue.6, pp.483-486, 2001.

J. Felsenstein, Evolutionary trees from dna sequences : a maximum likelihood approach, Journal of molecular evolution, vol.17, issue.6, pp.368-376, 1981.

G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache et al., Improved detection sensitivity in functional MRI data using a brain parcelling technique, International Conference on Medical Image Computing and Computer-assisted Intervention, pp.467-474, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615921

M. Fop and T. B. Murphy, Variable selection methods for model-based clustering, Statistics Surveys, vol.12, pp.18-65, 2018.

R. Fouchereau, G. Celeux, and P. Pamphile, Probabilistic modeling of s-n curves, International Journal of Fatigue, vol.68, pp.217-223, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00924080

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, Springer series in statistics, 2001.

K. Friston, C. Chu, J. Mourão-miranda, O. Hulme, G. Rees et al., Bayesian decoding of brain images, Neuroimage, vol.39, issue.1, pp.181-205, 2008.

K. J. Friston, A. P. Holmes, K. J. Worsley, J. Poline, C. D. Frith et al., Statistical parametric maps in functional imaging : a general linear approach, Human brain mapping, vol.2, issue.4, pp.189-210, 1994.

S. Frühwirth-schnatter, Finite mixture and Markov switching models, 2006.

P. Gaillard and Y. Goude, Forecasting electricity consumption by aggregating experts ; how to design a good set of experts. Dans Modeling and stochastic learning for forecasting in high dimensions, pp.95-115, 2015.

E. Gassiat and C. Keribin, The likelihood ratio test for the number of components in a mixture with markov regime, ESAIM : Probability and Statistics, vol.4, pp.25-52, 2000.

E. Gassiat and R. Van-handel, Consistent order estimation and minimal penalties, IEEE Transactions on Information Theory, vol.59, issue.2, pp.1115-1128, 2013.

S. Ghebreab, A. Smeulders, and P. Adriaans, Predicting brain states from fMRI data : Incremental functional principal component regression, Advances in Neural Information Processing Systems, pp.537-544, 2008.

C. Giraud, C. Calenge, C. Coron, and R. Julliard, Capitalising on opportunistic data for monitoring species relative abundances, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01021396

G. Govaert, Algorithme de classification d'un tableau de contingence, First international symposium on data analysis and informatics, pp.487-500, 1977.

G. Govaert, Simultaneous clustering of rows and columns, Control and Cybernetics, vol.24, pp.437-458, 1995.

G. Govaert and M. Nadif, Clustering with block mixture models, Pattern Recognition, vol.36, issue.2, pp.463-473, 2003.

G. Govaert and M. Nadif, Block clustering with Bernoulli mixture models : Comparison of different approaches, Computational Statistics & Data Analysis, vol.52, issue.6, pp.3233-3245, 2008.

G. Govaert and M. Nadif, Latent block model for contingency table, Communications in Statistics-Theory and Methods, vol.39, issue.3, pp.416-425, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00447792

G. Govaert and M. Nadif, Co-clustering, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00933301

A. Gunawardana and W. Byrne, Convergence theorems for generalized alternating minimization procedures, Journal of machine learning research, vol.6, pp.2049-2073, 2005.

M. Gyllenberg, T. Koski, E. Reilink, and M. Verlaan, Non-uniqueness in probabilistic numerical identification of bacteria, Journal of Applied Probability, vol.31, issue.2, pp.542-548, 1994.

R. Harpaz, W. Dumouchel, P. Lependu, A. Bauer-mehren, P. Ryan et al., Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system, Clinical Pharmacology & Therapeutics, vol.93, issue.6, pp.539-546, 2013.

C. Hennig, M. Meila, F. Murtagh, and R. Rocci, Handbook of cluster analysis, 2015.

C. Hernandez, C. Keribin, P. Drobinski, and E. S. Turquety, Statistical modelling of wildfire size and intensity : a step toward meteorological forecasting of summer extreme fire risk, Annales Geophysicae, vol.33, issue.12, pp.1495-1506, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01260501

L. Hubert and P. Arabie, Comparing partitions, Journal of classification, vol.2, issue.1, pp.193-218, 1985.

J. Jacques and C. Biernacki, Model-based co-clustering for ordinal data, Computational Statistics & Data Analysis, vol.123, pp.101-115, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01448299

C. Keribin, Estimation consistante de l'ordre de modèles de mélange, Comptes Rendus de l'Académie des Sciences-Series I-Mathematics, vol.326, issue.2, pp.243-248, 1998.

C. Keribin, Tests de modèles par maximum de vraisemblance, 1999.

C. Keribin, Consistent estimation of the order of mixture models, Sankhy? : The Indian Journal of Statistics, Series A, pp.49-66, 2000.

C. Keribin, Méthodes bayésiennes variationnelles : concepts et applications en neuroimagerie, Journal de la Société Française de Statistique, vol.151, issue.2, pp.107-131, 2010.

C. Keribin, Choix de modèles quand la vraisemblance est incalculable, 47èmes Journées de Statistique de la SFdS, 2015.

C. Keribin, A note on BIC and the slope heuristic, Journal de la SFdS, vol.160, issue.3, pp.136-139, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02391310

C. Keribin and D. Haughton, Asymptotic probabilities of over-estimating and underestimating the order of a model in general regular families, Communications in Statistics-Theory and Methods, vol.32, issue.7, pp.1373-1404, 2003.

C. Keribin, G. Govaert, and G. Celeux, Estimation d'un modèle à blocs latents par l'algorithme SEM, 42èmes Journées de Statistique, 2010.

C. Keribin, V. Brault, G. Celeux, and G. Govaert, Model selection for the binary latent block model, Proceedings of COMPSTAT, vol.2012, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00924210

C. Keribin, V. Brault, G. Celeux, and G. Govaert, Estimation and selection for the latent block model on categorical data, Statistics and Computing, vol.25, issue.6, pp.1201-1216, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00802764

C. Keribin, G. Celeux, and V. Robert, The latent block model : a useful model for high dimensional data, 61st ISI World Statistics Congress, ISI2017, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01957710

C. Keribin, Y. Liu, T. Popova, and Y. Rozenholc, A mixture model to characterize genomic alterations of tumors, Journal de la SFdS, vol.160, issue.1, pp.130-148, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02391289

N. Kriegeskorte, R. Goebel, and P. Bandettini, Information-based functional brain mapping, Proceedings of the National Academy of Sciences, vol.103, issue.10, pp.3863-3868, 2006.

C. Laclau and V. Brault, Noise-free latent block model for high dimensional data, Data Mining and Knowledge Discovery, vol.33, issue.2, pp.446-473, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01685777

P. Latouche and S. Robin, Bayesian model averaging of stochastic block models to estimate the graphon function and motif frequencies in a w-graph model, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00876334

P. Latouche, E. Birmelé, and C. Ambroise, Overlapping stochastic block models with application to the french political blogosphere, The Annals of Applied Statistics, vol.5, issue.1, pp.309-336, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00624538

D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, Advances in neural information processing systems, pp.556-562, 2001.

B. Li and S. C. Hoi, Online portfolio selection : A survey, ACM Computing Surveys (CSUR), vol.46, issue.3, p.35, 2014.

B. G. Lindsay, Mixture models : theory, geometry and applications, CBMS regional conference series in probability and statistics, p.163, 1995.

Y. Liu, C. Keribin, T. Popova, and Y. Rozenholc, Statistical estimation of genomic tumoral alterations, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01260716

A. Lomet, Sélection de modèles pour la classification de données continues, 2012.

B. Long, Z. M. Zhang, and P. S. Yu, Co-clustering by block value decomposition, ACM SIGKDD international conference on Knowledge discovery in data mining, pp.635-640, 2005.

M. Marbac, P. Tubert-bitter, and M. Sedki, Bayesian model selection in logistic regression for the detection of adverse drug reactions, Biometrical Journal, vol.58, issue.6, pp.1376-1389, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01140340

M. Mariadassou and C. Matias, Convergence of the groups posterior distribution in latent or stochastic block models, Bernoulli, vol.21, issue.1, pp.537-573, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00713120

M. Mariadassou, V. Brault, and C. Keribin, Normalité asymptotique de l'estimateur du maximum de vraisemblance dans le modèle de blocs latents, 2016.

P. Massart, Concentration inequalities and model selection, 2007.

L. Massoulié, Community detection thresholds and the weak Ramanujan property, Proceedings of the forty-sixth annual ACM symposium on Theory of computing, pp.694-703, 2014.

C. Maugis, G. Celeux, and M. Martin-magniette, Variable selection in model-based clustering : A general variable role modeling, Computational Statistics & Data Analysis, vol.53, issue.11, pp.3872-3882, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00342108

G. Mclachlan and T. Krishnan, The EM algorithm and extensions, vol.382, 2007.

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.

G. J. Mclachlan and K. E. Basford, Mixture models : Inference and applications to clustering, M. Dekker, vol.84, 1988.

V. Michel, Understanding the visual cortex by using classification techniques, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00550047

V. Michel, C. Damon, and B. Thirion, Mutual information-based feature selection enhances fMRI brain activity classification, 5th IEEE international symposium on biomedical imaging : From nano to macro, pp.592-595, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00504092

V. Michel, E. Eger, C. Keribin, and B. Thirion, Adaptive multi-class Bayesian sparse regression-an application to brain activity classification, MICCAI 2009 : fMRI data analysis workshop-Medical Image Computing and Computer Aided Intervention, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00504093

V. Michel, E. Eger, C. Keribin, J. Poline, and B. Thirion, A supervised clustering approach for extracting predictive information from brain activation images, Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, pp.7-14, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00504094

V. Michel, E. Eger, C. Keribin, and B. Thirion, Multi-class sparse Bayesian regression for neuroimaging data analysis, Machine Learning in Medical Imaging, pp.50-57, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00505057

V. Michel, E. Eger, C. Keribin, and B. Thirion, Multiclass sparse Bayesian regression for fMRI-based prediction, Journal of Biomedical Imaging, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00609365

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin et al., A supervised clustering approach for fMRI-based inference of brain states, Pattern Recognition, vol.45, issue.6, pp.2041-2049, 2012.
URL : https://hal.archives-ouvertes.fr/inria-00589201

G. W. Milligan, Clustering validation : results and implications for applied analyses, Clustering and classification, pp.341-375, 1996.

M. Palatucci and T. Mitchell, Classification in very high dimensional problems with handfuls of examples, pp.212-223, 2007.

T. Popova, E. Manié, D. Stoppa-lyonnet, G. Rigaill, E. Barillot et al., Genome alteration print (GAP) : a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays, Genome Biol, vol.10, issue.11, pp.128-128, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00663915

R. Rastelli, Exact ICL maximisation for the stochastic block transition model, Journal de la SFdS, vol.160, issue.1, pp.35-56, 2019.

C. Robert, The Bayesian choice : from decision-theoretic foundations to computational implementation, 2007.

V. Robert, Classification croisée pour l'analyse de bases de données de grandes dimensions de pharmacovigilance, 2017.

V. Robert, G. Celeux, and C. Keribin, Un modele statistique pour la pharmacovigilance, 47emes Journées de Statistique de la SFdS, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01255701

V. Robert, G. Celeux, C. Keribin, and P. Tubert-bitter, Modele des blocs latents et sélection de modeles en pharmacovigilance, vol.48, 2016.

S. Ryali, K. Supekar, D. A. Abrams, and V. Menon, Sparse logistic regression for whole-brain classification of fMRI data, NeuroImage, vol.51, issue.2, pp.752-764, 2010.

P. B. Ryan, M. J. Schuemie, E. Welebob, J. Duke, S. Valentine et al., Defining a reference set to support methodological research in drug safety, Drug safety, vol.36, issue.1, pp.33-47, 2013.

A. Samé, C. Ambroise, and G. Govaert, A classification EM algorithm for binned data, Computational statistics & data analysis, vol.51, issue.2, pp.466-480, 2006.

G. Schwarz, Estimating the dimension of a model. The annals of statistics, vol.6, pp.461-464, 1978.

M. Selosse, J. Jacques, and C. Biernacki, Model-based co-clustering for mixed type data, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01893457

H. Shan and A. Banerjee, Bayesian co-clustering, Eighth IEEE International Conference on Data Mining, pp.530-539, 2008.

Y. B. Slimen, S. Allio, and J. Jacques, Model-based co-clustering for functional data, Neurocomputing, vol.291, pp.97-108, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01383936

G. Stoltz and G. Lugosi, Internal regret in on-line portfolio selection, Machine Learning, vol.59, pp.125-159, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00007535

H. Teicher, On the mixture of distributions, The Annals of Mathematical Statistics, vol.31, issue.1, pp.55-73, 1960.

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization : Multi-subject parcellation of fMRI datasets, Human brain mapping, vol.27, issue.8, pp.678-693, 2006.

B. Thyreau, B. Thirion, G. Flandin, and J. Poline, Anatomo-functional description of the brain : a probabilistic approach, IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol.5, 2006.

M. E. Tipping, The relevance vector machine, Advances in neural information processing systems, pp.652-658, 2000.

T. Tokuda, J. Yoshimoto, Y. Shimizu, K. Yoshida, S. Toki et al., Bayesian multiple and co-clustering methods : Application to fMRi data, IPSJ SIG Notes, vol.176, issue.28, pp.1-5, 2014.

E. P. Van-puijenbroek, A. Bate, H. G. Leufkens, M. Lindquist, R. Orre et al., A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions, Pharmacoepidemiology and drug safety, vol.11, issue.1, pp.3-10, 2002.

G. Varoquaux, A. Gramfort, J. Poline, and B. Thirion, Brain covariance selection : better individual functional connectivity models using population prior, Advances in neural information processing systems, pp.2334-2342, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00512451

Y. R. Wang and P. J. Bickel, Likelihood-based model selection for stochastic block models, The Annals of Statistics, vol.45, issue.2, pp.500-528, 2017.

J. Wu, Model-based clustering and model selection for binned data, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01142358

J. Wyse and N. Friel, Block clustering with collapsed latent block models, Statistics and Computing, vol.22, issue.2, pp.415-428, 2012.

J. Yoo and S. Choi, Orthogonal nonnegative matrix tri-factorization for co-clustering : Multiplicative updates on stiefel manifolds. Information processing & management, vol.46, pp.559-570, 2010.

G. Youness and G. Saporta, Une méthodologie pour la comparaison de partitions, vol.52, pp.97-120, 2004.