G. Celeux and G. Soromenho, An entropy criterion for assessing the number of clusters in a mixture model, Journal of Classification, vol.5, issue.2, pp.195-212, 1996.
DOI : 10.1007/BF01246098
URL : https://hal.archives-ouvertes.fr/inria-00074799

T. Cover, T. , and J. , Elements of Information Theory, 2006.

J. Durand, P. Gonçalvès, and Y. Guédon, Computational Methods for Hidden Markov Tree Models???An Application to Wavelet Trees, IEEE Transactions on Signal Processing, vol.52, issue.9, pp.2551-2560, 2004.
DOI : 10.1109/TSP.2004.832006
URL : https://hal.archives-ouvertes.fr/hal-00830078

Y. Ephraim and N. Merhav, Hidden Markov processes, IEEE Transactions on Information Theory, vol.48, issue.6, pp.1518-1569, 2002.
DOI : 10.1109/TIT.2002.1003838

Y. Guédon, Exploring the state sequence space for hidden Markov and semi-Markov chains, Computational Statistics & Data Analysis, vol.51, issue.5, pp.2379-2409, 2007.
DOI : 10.1016/j.csda.2006.03.015

D. Hernando, V. Crespi, C. , and G. , Efficient Computation of the Hidden Markov Model Entropy for a Given Observation Sequence, IEEE Transactions on Information Theory, vol.51, issue.7, pp.2681-2685, 2005.
DOI : 10.1109/TIT.2005.850223

S. Lauritzen, Graphical Models, 1996.

G. Mclachlan and D. Peel, Finite Mixture Models, Series in Probability and Statistics, 2000.
DOI : 10.1002/0471721182