R. M. Altman, Mixed Hidden Markov Models, Journal of the American Statistical Association, vol.102, issue.477, pp.201-210, 2007.
DOI : 10.1198/016214506000001086

G. E. Archer and D. M. Titterington, Parameter estimation for hidden Markov chains, Journal of Statistical Planning and Inference, vol.108, issue.1-2, pp.365-390, 2002.
DOI : 10.1016/S0378-3758(02)00318-X

D. Barthélémy and Y. Caraglio, Plant Architecture: A Dynamic, Multilevel and Comprehensive Approach to Plant Form, Structure and Ontogeny, Annals of Botany, vol.99, issue.3, pp.375-407, 2007.
DOI : 10.1093/aob/mcl260

B. S. Caffo, W. Jank, and G. L. Jones, Ascent-based Monte Carlo expectation- maximization, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.11, issue.2, pp.235-251, 2005.
DOI : 10.1111/1467-9868.00334

S. G. Candy, Estimation in Forest Yield Models Using Composite Link Functions with Random Effects, Biometrics, vol.53, issue.1, pp.146-160, 1997.
DOI : 10.2307/2533104

O. Cappé, E. Moulines, and T. Rydén, Inference in hidden Markov models. Springer Series in Statistics, 2005.

G. Celeux, O. Martin, and C. Lavergne, Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments, Statistical Modelling, vol.5, issue.3, pp.243-267, 2005.
DOI : 10.1191/1471082X05st096oa
URL : https://hal.archives-ouvertes.fr/hal-00192771

F. Chaubert, Y. Caraglio, C. Lavergne, C. Trottier, and Y. Guédon, A statistical model for analyzing jointly growth phases, the influence of environmental factors and inter-individual heterogeneity. Applications to forest trees, 5th International Workshop on Functional-Structural Plant Models, pp.43-44, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00192795

F. Chaubert-pereira, Y. Caraglio, C. Lavergne, and Y. Guédon, Identifying ontogenetic, environmental and individual components of forest tree growth, Annals of Botany, vol.104, issue.5, 2008.
DOI : 10.1093/aob/mcp189
URL : https://hal.archives-ouvertes.fr/hal-00423945

S. Chib, Calculating posterior distributions and modal estimates in Markov mixture models, Journal of Econometrics, vol.75, issue.1, pp.79-97, 1996.
DOI : 10.1016/0304-4076(95)01770-4

J. B. Durand, Y. Guédon, Y. Caraglio, and E. Costes, Analysis of the plant architecture via tree-structured statistical models: the hidden Markov tree models, New Phytologist, vol.10, issue.3, pp.813-825, 2005.
DOI : 10.1111/j.1469-8137.2005.01405.x
URL : https://hal.archives-ouvertes.fr/hal-00017402

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

S. Fruhwirth-schnatter, Finite Mixture and Markov Switching models. Springer Series in Statistics, 2006.

Y. Guédon, Estimating Hidden Semi-Markov Chains From Discrete Sequences, Journal of Computational and Graphical Statistics, vol.12, issue.3, pp.604-639, 2003.
DOI : 10.1198/1061860032030

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

Y. Guédon, Y. Caraglio, P. Heuret, E. Lebarbier, and C. Meredieu, Analyzing growth components in trees, Journal of Theoretical Biology, vol.248, issue.3, pp.418-447, 2007.
DOI : 10.1016/j.jtbi.2007.05.029

M. Gupta, P. Qu, and J. G. Ibrahim, A temporal hidden Markov regression model for the analysis of gene regulatory networks, Biostatistics, vol.8, issue.4, pp.805-820, 2007.
DOI : 10.1093/biostatistics/kxm007

S. Kim and P. Smyth, Segmental Hidden Markov Models with Random effects for Waveform Modeling, Journal of Machine Learning Research, vol.7, pp.945-969, 2006.

C. Lavergne, M. J. Martinez, and C. Trottier, Finite mixture models for exponential repeated data, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00129777

G. Lindgren, Markov regime models for mixed distributions and switching regressions, Scandinavian Journal of Statistics, Theory and Applications, vol.5, pp.81-91, 1978.

C. E. Mcculloch, Maximum Likelihood Algorithms for Generalized Linear Mixed Models, Journal of the American Statistical Association, vol.86, issue.437, pp.162-170, 1997.
DOI : 10.1080/01621459.1997.10473613

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions Wiley Series in Probability and Statistics, 2008.

X. L. Meng and S. Schilling, Fitting Full-Information Item Factor Models and an Empirical Investigation of Bridge Sampling, Journal of the American Statistical Association, vol.6, issue.435, pp.911254-1267, 1996.
DOI : 10.2307/2290005

R. M. Neal and G. E. Hinton, A view of the EM algorithm that justifies incremental , sparse, and other variants Learning in graphical models, Proceedings of the NATO ASI, Ettore Maiorana Centre NATO ASI Series. Series D. Behavioural and Social Sciences, pp.355-368, 1996.

Y. Ninomiya and A. Yoshimoto, Statistical Method for Detecting Structural Change in the Growth Process, Biometrics, vol.87, issue.1, pp.46-53, 2008.
DOI : 10.1111/j.1541-0420.2007.00844.x

W. Qian and D. M. Titterington, Estimation of Parameters in Hidden Markov Models, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.337, issue.1647, pp.407-428, 1647.
DOI : 10.1098/rsta.1991.0132

F. Rijmen, E. H. Ip, S. Rapp, and E. G. Shaw, Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.4, issue.3, pp.739-753, 2008.
DOI : 10.1002/1097-0142(19950301)75:5<1151::AID-CNCR2820750515>3.0.CO;2-Q

J. Q. Shi and S. Y. Lee, Latent variable models with mixed continuous and polytomous data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.1, pp.77-87, 2000.
DOI : 10.1111/1467-9868.00220

P. Smyth, D. Heckerman, and M. I. Jordan, Probabilistic Independence Networks for Hidden Markov Probability Models, Neural Computation, vol.1994, issue.2, pp.227-269, 1997.
DOI : 10.1016/0262-8856(94)90010-8

G. Wei and M. Tanner, A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, vol.51, issue.411, pp.699-704, 1990.
DOI : 10.1214/aos/1176346060