C. Archambeau and M. Verleysen, Robust Bayesian clustering, Neural Networks, vol.20, issue.1, pp.129-138, 2007.

A. Arnaud, F. Forbes, R. Steele, B. Lemasson, and E. L. Barbier, Bayesian mixtures of multiple scale distributions, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01941682

H. Attias, Inferring Parameters and Structure of Latent Variable Models by Variational Bayes, UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp.21-30, 1999.

H. Attias, A variational Bayesian framework for graphical models, Proc. Advances in Neural Information Processing Systems 12, pp.209-215, 2000.

J. Banfield and A. Raftery, Model-Based Gaussian and Non-Gaussian Clustering, Biometrics, vol.49, issue.3, pp.803-821, 1993.

J. P. Baudry, E. A. Raftery, G. Celeux, K. Lo, and R. Gottardo, Combining mixture components for clustering, Journal of Computational and Graphical Statistics, vol.19, issue.2, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00321090

J. P. Baudry, C. Maugis, and B. Michel, Slope heuristics: overview and implementation, Statistics and Computing, vol.22, issue.2, pp.455-470, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00461639

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

G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, vol.28, issue.5, pp.781-793, 1995.
URL : https://hal.archives-ouvertes.fr/inria-00074643

G. Celeux, S. Fruhwirth-schnatter, and C. Robert, Model Selection for Mixture Models-Perspectives and Strategies. Handbook of Mixture Analysis, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01961077

A. Corduneanu and C. Bishop, Variational Bayesian Model Selection for Mixture Distributions, Proceedings Eighth International Conference on Artificial Intelligence and Statistics, p.2734, 2001.

D. B. Dahl, Model-based clustering for expression data via a Dirichlet process mixture model, in Bayesian Inference for, Gene Expression and Proteomics, 2006.

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.381-396, 2002.

F. Forbes and D. Wraith, A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweights: Application to robust clustering, Statistics and Computing, vol.24, issue.6, pp.971-984, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00823451

A. Fritsch and K. Ickstadt, Improved criteria for clustering based on the posterior similarity matrix, Bayesian Analysis, vol.4, issue.2, pp.367-391, 2009.

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

D. Gorur and C. Rasmussen, Dirichlet process Gaussian mixture models: Choice of the base distribution, Journal of Computer Science and Technology, vol.25, issue.4, pp.653-664, 2010.

C. Hennig, Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, vol.4, issue.1, pp.3-34, 2010.

P. D. Hoff, A Hierarchical Eigenmodel for Pooled Covariance Estimation, Journal of the Royal Statistical Society. Series B (Statistical Methodology), vol.71, issue.5, pp.971-992, 2009.

N. L. Johnson, S. Kotz, and N. Balakrishnan, Continuous Univariate Distributions, vol.2, 1994.

G. Malsiner-walli, S. Schnatter, and B. Grün, Model-based clustering based on sparse finite Gaussian mixtures, Statistics and Computing, vol.26, issue.1, pp.303-324, 2016.

C. A. Mcgrory and D. M. Titterington, Variational Approximations in Bayesian Model Selection for Finite Mixture Distributions, Comput. Stat. Data Anal, vol.51, issue.11, pp.5352-5367, 2007.

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
URL : https://hal.archives-ouvertes.fr/hal-02415068

V. Melnykov, Merging mixture components for clustering through pairwise overlap, Journal of Computational and Graphical Statistics, 2014.

C. E. Rasmussen, The infinite Gaussian mixture model, In: NIPS. vol, vol.12, pp.554-560, 1999.

S. Richardson and P. J. Green, On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion), Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.4, pp.731-792, 1997.

J. Rousseau and K. Mengersen, Asymptotic behaviour of the posterior distribution in overfitted mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.73, issue.5, pp.689-710, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00641475

L. Scrucca, M. Fop, T. B. Murphy, and A. Raftery, mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, vol.8, issue.1, pp.205-233, 2016.

K. Tu, Modified Dirichlet Distribution: Allowing Negative Parameters to Induce Stronger Sparsity, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.1986-1991, 2016.

J. Verbeek, N. Vlassis, and B. Kröse, Efficient Greedy Learning of Gaussian Mixture Models, Neural Computation, vol.15, issue.2, pp.469-485, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00321487

X. Wei and C. Li, The infinite student t-mixture for robust modeling, Signal Processing, vol.92, issue.1, pp.224-234, 2012.

H. Z. Yerebakan, B. Rajwa, and M. Dundar, The infinite mixture of infinite Gaussian mixtures, Advances in Neural Information Processing Systems, pp.28-36, 2014.