References:

C. Hu, W. Fan, J. Du, Y. Zeng Model-based segmentation of image data using spatially constrained mixture models Neurocomputing, 283 (2018), pp. 214-227

A.K. Jain, R.P.W. Duin, J. Mao Statistical pattern recognition: A review IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (1) (2000), pp. 4-37

A.K. Jain, M.N. Murty, P.J. Flynn Data clustering: a review ACM Computing Surveys, 31 (3) (1999), pp. 264-323

G.J. McLachlan, D. Peel Finite Mixture Models New York: Wiley (2000)

S. Boutemedjet, D. Ziou, N. Bouguila Model-based subspace clustering of non-gaussian data Neurocomputing, 73 (10) (2010), pp. 1730-1739

Y. Lai, Y. Ping, K. Xiao, B. Hao, X. ZhangVariational bayesian inference for a dirichlet process mixture of beta distributions and application Neurocomputing, 278 (2018), pp. 23-33

G. Zhou, D. Zhu, Y. Wei, Z. Wang, Y. Zhou Real-time online learning of gaussian mixture model for opacity mapping Neurocomputing, 211 (2016), pp. 212-220

W. Fan, N. Bouguila, D. Ziou Variational learning of finite Dirichlet mixture models using component splitting Neurocomputing, 129 (2014), pp. 3-16

W. Fan, N. Bouguila Online variational learning of generalized Dirichlet mixture models with feature selection Neurocomputing, 126 (2014), pp. 166-179

T. Bdiri, N. Bouguila Positive vectors clustering using inverted Dirichlet finite mixture models Expert Systems with Applications, 39 (2) (2012), pp. 1869-1882

T. Bdiri, N. Bouguila Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation Neural Computing and Applications, 23 (5) (2013), pp. 1443-1458

K.T. Fang, S. Kotz, K.W. Ng Symmetric Multivariate and Related Distributions Chapman and Hall (1990)

S. Ganesalingam Classification and mixture approaches to clustering via maximum likelihood Journal of the Royal Statistical Society, 38 (3) (1989), pp. 455-466

G.J. Mclachlan, T. Krishnan The EM algorithm and extensions Biometrics, 382 (1) (1997), pp. 154-156

H. Akaike A New Look at the Statistical Model Identification Springer New York (1974)

G. Schwarz Estimating dimension of a model Annals of Statistics (6) (1978), pp. 461-464

J. Rissanen Modeling by shortest data description Pergamon Press, Inc. (1978)

C.S. Wallace, D.M. Boulton An information measure for classification Computer Journal, 11 (2) (1968), pp. 185-194

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

N. Bouguila, D. Ziou High-dimensional unsupervised selection and estimation of a finite generalized Dirichlet mixture model based on minimum message length IEEE Trans. Pattern Anal. Mach. Intell., 29 (10) (2007), pp. 1716-1731

N. Bouguila, D. Ziou Unsupervised selection of a finite Dirichlet mixture model: An MML-based approach IEEE Trans. Knowl. Data Eng., 18 (8) (2006), pp. 993-1009

H. Attias A variational Bayesian framework for graphical models International Conference on Neural Information Processing Systems (1999), pp. 209-215

M.I. Jordan, Z. Ghahramani, T.S. Jaakkola, L.K. Saul An introduction to variational methods for graphical models Machine Learning, 37 (2) (1999), pp. 183-233

C. Bishop Pattern Recognition and Machine Learning Springer (2006)

S. Konishi, G. Kitagawa Information Criteria and Statistical Modeling Springer New York (2008)

C.S. Wallace Statistical and Inductive Inference by Minimum Message Length Springer-Verlag New York (2005)

C.E. Shannon A mathematical theory of communication The Bell System Technical Journal, 27 (4) (1948), pp. 623-656

W. Fan, N. Bouguila, D. Ziou Variational learning for finite Dirichlet mixture models and applications. IEEE Transactions on Neural Networks and Learning Systems, 23 (5) (2012), pp. 762-774

C.M. Bishop, N. Lawrence, T. Jaakkola, M.I. Jordan Approximating posterior distributions in belief networks using mixtures Conference on Advances in Neural Information Processing Systems (1998), pp. 416-422

N.D. Lawrence, C.M. Bishop, M.I. Jordan Mixture representations for inference and learning in boltzmann machines Fourteenth Conference on Uncertainty in Artificial Intelligence (1998), pp. 320-327

M.M. Ichir, A. Mohammad-Djafari A mean field approximation approach to blind source separation with lp priors 13th European Signal Processing Conference (2005), pp. 1-4

P. Kasarapu, L. Allison Minimum message length estimation of mixtures of multivariate gaussian and von mises-fisher distributions Machine Learning, 100 (2) (2015), pp. 333-378

N. Nasios, A.G. Bors Variational learning for gaussian mixture models IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36 (4) (2006), pp. 849-862

N. Bouguila Hybrid generative/discriminative approaches for proportional data modeling and classification IEEE Trans. Knowl. Data Eng., 24 (12) (2012), pp. 2184-2202

W. Fan, N. Bouguila Learning finite Beta-Liouville mixture models via variational Bayes for proportional data clustering Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI) (2013), pp. 1323-1329

D. Mann, D. Zipes, P. Libby, R. Bonow Braunwald’s heart disease : a textbook of cardiovascular medicine (10th), Elsevier (2014)

R. Alizadehsani, J. Habibi, M.J. Hosseini, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, B. Bahadorian, Z.A. SaniA data mining approach for diagnosis of coronary artery disease
Computer Methods and Programs in Biomedicine, 111 (1) (2013), pp. 52-61

N.D. Singpurwalla, S.P. Wilson Software reliability modeling International Statistical Review, 62 (3) (1994), pp. 289-317

M.R. Lyu Handbook of software reliability engineering McGraw-Hill, Inc. (1996)

F.A. Graybill Matrices With Applications in Statistics Wadsworth (1983)

C.S. Wallace, D.L. Dowe Mml mixture modelling of multi-state, poisson, von mises circular and gaussian distributions Proc. 6th Int. Workshop on Artif. Intelligence and Statistics (1997), pp. 529-536

R.A. Baxter, J.J. Oliver Finding overlapping components with MML Statistics and Computing, 10 (1) (2000), pp. 5-16