P. R. Amestoy, T. A. Davis, and I. S. Duff, An Approximate Minimum Degree Ordering Algorithm, SIAM Journal on Matrix Analysis and Applications, vol.17, issue.4, pp.886-905, 1996.
DOI : 10.1137/S0895479894278952

D. Andersson, M. Madigan, and . Perlman, Alternative Markov Properties for Chain Graphs, Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence, pp.40-48, 1996.
DOI : 10.1111/1467-9469.00224

L. Banerjee, A. Ghaoui, and . Aspremont, Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data, The Journal of Machine Learning Research, vol.9, pp.485-516, 2008.

A. Berry, J. R. Blair, P. Heggernes, and B. W. Peyton, Maximum Cardinality Search for Computing Minimal Triangulations of Graphs, Algorithmica, vol.39, issue.4, pp.287-298, 2004.
DOI : 10.1007/s00453-004-1084-3

C. Bron and J. Kerbosch, Algorithm 457: finding all cliques of an undirected graph, Communications of the ACM, vol.16, issue.9, pp.575-577, 1973.
DOI : 10.1145/362342.362367

. Buntine, Theory Refinement on Bayesian Networks, Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence, pp.52-60, 1991.
DOI : 10.1016/B978-1-55860-203-8.50010-3

. Buntine, A guide to the literature on learning probabilistic networks from data, IEEE Transactions on Knowledge and Data Engineering, vol.8, issue.2, pp.195-210, 1996.
DOI : 10.1109/69.494161

F. Cazals and C. Karande, A note on the problem of reporting maximal cliques, Theoretical Computer Science, vol.407, issue.1-3, pp.564-568, 2008.
DOI : 10.1016/j.tcs.2008.05.010

D. Chickering, Learning equivalence classes of Bayesian-network structures, The Journal of Machine Learning Research, vol.2, pp.445-498, 2002.

D. Cox and N. Wermuth, Linear Dependencies Represented by Chain Graphs, Statistical Science, vol.8, issue.3, pp.204-218, 1993.
DOI : 10.1214/ss/1177010887

J. Dahl, V. Roychowdhury, and L. Vandenberghe, Maximum likelihood estimation of Gaussian graphical models: numerical implementation and topology selection, p.39, 2005.

M. De-campos and J. M. Puerta, Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.228-239, 2001.
DOI : 10.1007/3-540-44652-4_21

A. P. Dempster, Covariance Selection, Biometrics, vol.28, issue.1, pp.157-175, 1972.
DOI : 10.2307/2528966

M. Drton, Discrete chain graph models, Bernoulli, vol.15, issue.3, pp.736-753, 2009.
DOI : 10.3150/08-BEJ172

M. Drton and M. D. Perlman, A SINful approach to Gaussian graphical model selection, Journal of Statistical Planning and Inference, vol.138, issue.4, pp.1179-1200, 2008.
DOI : 10.1016/j.jspi.2007.05.035

P. Eades, A heuristics for graph drawing, Congressus Numerantium, vol.42, issue.13, pp.146-160, 1984.

. Edwards, Introduction to graphical modelling, p.40, 2000.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.
DOI : 10.1093/biostatistics/kxm045

N. Friedman and M. Goldszmidt, Sequential update of Bayesian network structure, Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, pp.165-174, 1997.

N. Friedman, I. Nachman, and D. Peer, Learning Bayesian network structure from massive datasets: the sparse candidate algorithm, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp.206-215, 1999.

M. Fruchterman and E. M. Reingold, Graph drawing by force-directed placement . Software: Practice and experience, pp.1129-1164, 1991.

M. Frydenberg, The chain graph Markov property, Scandinavian Journal of Statistics, vol.17, pp.333-353, 1990.

J. A. Gamez, J. L. Mateo, and J. M. Puerta, Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood, Data Mining and Knowledge Discovery, vol.8, issue.4, pp.106-148, 2011.
DOI : 10.1007/s10618-010-0178-6

D. Heckerman, D. Geiger, and D. M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, pp.197-243, 1995.

J. D. Hunter, Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007.
DOI : 10.1109/MCSE.2007.55

D. G. Kirkpatrick, M. P. Jr, and . Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

S. G. Kobourov, Spring embedders and force directed graph drawing algorithms, chapter 12, pp.383-408

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, p.35, 2009.

L. Lauritzen and N. Wermuth, Graphical models for associations between variables, some of which are qualitative and some quantitative. The Annals of Statistics, pp.31-57, 1989.

Z. Ma, X. Xie, and Z. Geng, Structural learning of chain graphs via decomposition, Journal of machine learning research: JMLR, vol.9, pp.2847-2887, 2008.

N. Meinshausen and P. Buhlmann, High-dimensional graphs and variable selection with the Lasso. The Annals of Statistics, pp.1436-1462, 2006.

J. Moussouris, Gibbs and Markov random systems with constraints, Journal of Statistical Physics, vol.5, issue.13, pp.11-33, 1974.
DOI : 10.1007/BF01011714

R. E. Neapolitan, Learning Bayesian Networks, p.40, 2004.
DOI : 10.1016/B978-012370477-1.50021-9

J. Rose, R. E. Tarjan, and G. S. Lueker, Algorithmic Aspects of Vertex Elimination on Graphs, SIAM Journal on Computing, vol.5, issue.2, pp.266-283, 1976.
DOI : 10.1137/0205021

T. Speed and H. Kiiveri, Gaussian Markov distributions over finite graphs. The Annals of Statistics, pp.138-150, 1986.

P. Spirtes, C. N. Glymour, and R. Scheines, Causation, prediction, and search, p.40, 2000.
DOI : 10.1007/978-1-4612-2748-9

K. Sugiyama and K. Misue, Graph Drawing by the Magnetic Spring Model, Journal of Visual Languages & Computing, vol.6, issue.3, pp.217-231, 1995.
DOI : 10.1006/jvlc.1995.1013

. Tamassia, Handbook of Graph Drawing and Visualization (Discrete Mathematics and Its Applications) Chapman & Hall/CRC, p.13, 2007.

R. Tarjan, Depth-First Search and Linear Graph Algorithms, SIAM Journal on Computing, vol.1, issue.2, pp.146-160, 1972.
DOI : 10.1137/0201010

. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B, vol.58, issue.1, pp.267-288, 1996.

E. Tomita, A. Tanaka, and H. Takahashi, The worst-case time complexity for generating all maximal cliques and computational experiments, Theoretical Computer Science, vol.363, issue.1, pp.28-42, 2006.
DOI : 10.1016/j.tcs.2006.06.015

N. Wermuth, Linear Recursive Equations, Covariance Selection, and Path Analysis, Journal of the American Statistical Association, vol.5, issue.372, pp.963-972, 1980.
DOI : 10.1080/01621459.1980.10477580

N. Wermuth, On block-recursive linear regression equations, Reuista Brasileim de Probabilidade Estattstic, vol.6, issue.38, pp.1-56, 1992.

N. Wermuth and S. L. Lauritzen, On substantive research hypotheses, conditional independence graphs and graphical chain models, Journal of the Royal Statistical Society. Series B (Methodological), vol.52, issue.1, pp.21-50, 1990.

E. Yang, P. Ravikumar, G. Allen, and Z. Liu, Graphical models via generalized linear models, Advances in Neural Information Processing Systems 25, pp.1367-1375, 2012.

R. References, N. Johnson, S. Kotz, and N. Balakrishnan, Discrete multivariate distributions, p.61, 1997.

M. Kimmel and D. E. Axelrod, Branching processes in biology. interdisciplinary applied mathematics 19, p.60, 2002.

J. Legrand, Vers une compréhension multi-´ echelle du development floral: des réseaux auxiniques aux patrons de la dynamique cellulaire, pp.2014-53

W. Nultsch, Botanique générale, p.52, 1998.

V. Olariu, D. Coca, S. A. Billings, P. Tonge, P. Gokhale et al., Modified variational Bayes EM estimation of hidden Markov tree model of cell lineages, Bioinformatics, vol.25, issue.21, pp.2824-2830, 2009.
DOI : 10.1093/bioinformatics/btp456

F. Ramírez and T. L. Davenport, Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010.
DOI : 10.1016/j.scienta.2010.06.024

M. Reingold and J. Tilford, Tidier Drawings of Trees, IEEE Transactions on Software Engineering, vol.7, issue.2, pp.7223-228, 1981.
DOI : 10.1109/TSE.1981.234519

S. Sabatier, D. Barthélémy, I. Ducousso, and . Germain, Modalit??s d'allongement et morphologie des pousses annuelles chez le noyer commun, <I>Juglans regia</I> L. 'Lara' (Juglandaceae), Canadian Journal of Botany, vol.76, issue.7, pp.1253-1264, 1998.
DOI : 10.1139/cjb-76-7-1253

K. Sugiyama and K. Misue, Graph Drawing by the Magnetic Spring Model, Journal of Visual Languages & Computing, vol.6, issue.3, pp.217-231, 1995.
DOI : 10.1006/jvlc.1995.1013

. Tamassia, Handbook of Graph Drawing and Visualization (Discrete Mathematics and Its Applications) Chapman & Hall/CRC, p.50, 2007.

J. Q. Walker, A node-positioning algorithm for general trees Software: Practice and Experience, pp.685-705, 1990.

D. References, A. Bacciu, A. Micheli, and . Sperduti, Bottom-up generative modeling of treestructured data, Neural Information Processing. Theory and Algorithms, pp.660-668, 2010.

L. E. Baum, T. Petrie, G. Soules, and N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The annals of Mathematical Statistics, pp.164-171, 1970.

G. Claeskens and N. L. Hjort, Model selection and model averaging, p.75, 2008.

M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, Wavelet-based statistical signal processing using hidden Markov models, IEEE Transactions on Signal Processing, vol.46, issue.4, pp.886-902, 1998.
DOI : 10.1109/78.668544

P. Das, T. Ito, F. Wellmer, T. Vernoux, A. Dedieu et al., Floral stem cell termination involves the direct regulation of AGAMOUS by PERIANTHIA, Development, vol.136, issue.10, pp.1605-1611, 2009.
DOI : 10.1242/dev.035436
URL : https://hal.archives-ouvertes.fr/hal-00412931

J. Durand, P. Goncalvès, 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

J. 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

T. E. Harris, The theory of branching processes, p.71, 2002.
DOI : 10.1007/978-3-642-51866-9

S. Johnson, N. Kotz, and . Balakrishnan, Discrete multivariate distributions, p.85, 1997.

G. Mclachlan and T. Krishnan, The EM algorithm and extensions, p.75, 2007.

G. Mclachlan and D. Peel, Finite mixture models, p.73, 2000.
DOI : 10.1002/0471721182

O. Ronen, J. Rohlicek, and M. Ostendorf, Parameter estimation of dependence tree models using the EM algorithm, IEEE Signal Processing Letters, vol.2, issue.8, pp.157-159, 1995.
DOI : 10.1109/97.404132

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

D. R. Smyth, J. L. Bowman, and E. M. Meyerowitz, Early flower development in Arabidopsis . The Plant Cell Online, pp.755-767, 1990.

W. Tong and C. Tang, Robust estimation of adaptive tensors of curvature by tensor voting, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.3, pp.434-449, 2005.
DOI : 10.1109/TPAMI.2005.62

H. W. Watson and F. Galton, On the probability of the extinction of families. The Journal of the Anthropological Institute of Great Britain and Ireland, pp.138-144

G. C. Wei and M. A. 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

G. I. References, Z. Allen, and . Liu, A log-linear graphical model for inferring genetic networks from high-throughput sequencing data, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1-6

. Chacko, Physiology of vegetative and reproductive growth in mango (Mangifera indica L.) trees, Proceedings of the First Australian Mango Research Workshop, pp.54-70, 1986.

D. Chickering, Learning equivalence classes of Bayesian-network structures, The Journal of Machine Learning Research, vol.2, pp.445-498, 2002.

D. Chickering, Optimal structure identification with greedy search, The Journal of Machine Learning Research, vol.3, pp.507-554, 2003.

A. Dambreville, P. Fernique, C. Pradal, P. Lauri, F. Normand et al., Deciphering mango tree asynchronisms using Markov tree and probabilistic graphical models, FSPM2013 -7th International Workshop on Functional- Structural Plant Models, pp.210-212
URL : https://hal.archives-ouvertes.fr/hal-01269304

D. Edwards, Introduction to graphical modelling, p.90, 2000.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.
DOI : 10.1093/biostatistics/kxm045

W. R. Gilks, Markov Chain Monte Carlo, 2005.
DOI : 10.1002/0470011815.b2a14021

P. Haccou, P. Jagers, and V. A. Vatutin, Branching processes: variation, growth, and extinction of populations, p.91, 2005.
DOI : 10.1017/CBO9780511629136

A. Johnson, S. Kemp, and . Kotz, Univariate discrete distributions, p.95, 1993.
DOI : 10.1002/0471715816

S. Johnson, N. Kotz, and . Balakrishnan, Discrete multivariate distributions, pp.92-96, 1997.

D. Karlis, An EM algorithm for multivariate Poisson distribution and related models, Journal of Applied Statistics, vol.30, issue.1, pp.63-77, 2003.
DOI : 10.1080/0266476022000018510

D. Karlis and L. Meligkotsidou, Multivariate Poisson regression with covariance structure, Statistics and Computing, vol.15, issue.4, pp.255-265, 2005.
DOI : 10.1007/s11222-005-4069-4

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, p.96, 2009.

Z. Ma, X. Xie, and Z. Geng, Structural learning of chain graphs via decomposition, Journal of machine learning research: JMLR, vol.9, pp.2847-90, 2008.

K. V. Mardia, Families of bivariate distributions, p.92, 1970.

P. Mccullagh and J. Nelder, Generalized linear models. Monographs on Statistics and Applied Probability 37, p.94, 1989.

F. Ramírez and T. L. Davenport, Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010.
DOI : 10.1016/j.scienta.2010.06.024

R. Robinson, Counting labeled acyclic digraphs, Combinatorial mathematics V, pp.28-43, 1973.

. Steinsky, Enumeration of labelled chain graphs and labelled essential directed acyclic graphs, Discrete Mathematics, vol.270, issue.1-3, pp.267-278, 2003.
DOI : 10.1016/S0012-365X(02)00838-5

E. Yang, P. Ravikumar, G. Allen, and Z. Liu, Graphical models via generalized linear models, Advances in Neural Information Processing Systems 25, pp.1367-1375, 2012.

E. Yang, Y. Baker, P. Ravikumar, G. Allen, and Z. Liu, Mixed graphical models via exponential families, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, pp.1042-1050, 2014.

S. Yang and K. Chang, Comparison of score metrics for Bayesian network learning, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929), pp.419-428, 2002.
DOI : 10.1109/ICSMC.1996.565479

W. Yang, Some limit properties for Markov chains indexed by a homogeneous tree, Statistics & Probability Letters, vol.65, issue.3, pp.241-250, 2003.
DOI : 10.1016/j.spl.2003.04.001

R. References, J. Baudry, C. Maugis, and B. Michel, Slope heuristics: overview and implementation, Statistics and Computing, vol.22, issue.2, pp.455-470, 2012.

. Chacko, Physiology of vegetative and reproductive growth in mango (Mangifera indica L.) trees, Proceedings of the First Australian Mango Research Workshop, pp.54-70, 1986.

. Dambreville, . Croissance, and . Développement-du-manguier, Mangifera indica L.) in natura: approche expérimentale et modélisation de l'influence d'un facteur exogène, la température, et de facteurs endogènes architecturaux, II-Sciences et Techniques du Languedoc, 2012. URL http, p.116
URL : https://hal.archives-ouvertes.fr/tel-00860484

L. Denoeud and A. Guénoche, Comparison of Distance Indices Between Partitions, Data Science and Classification, pp.21-28, 2006.
DOI : 10.1007/3-540-34416-0_3

P. Ferraro and C. Godin, An Edit Distance between Quotiented Trees, Algorithmica, vol.36, issue.1, pp.1-39, 2003.
DOI : 10.1007/s00453-002-1002-5
URL : https://hal.archives-ouvertes.fr/hal-00307409

C. Godin and Y. Caraglio, A Multiscale Model of Plant Topological Structures, Journal of Theoretical Biology, vol.191, issue.1, pp.1-46, 1998.
DOI : 10.1006/jtbi.1997.0561
URL : https://hal.archives-ouvertes.fr/hal-00827484

M. Hawkins, Point Estimation of the Parameters of Piecewise Regression Models, Applied Statistics, vol.25, issue.1, pp.51-57, 1976.
DOI : 10.2307/2346519

P. Hupé, N. Stransky, J. Thiery, F. Radvanyi, and E. Barillot, Analysis of array CGH data: from signal ratio to gain and loss of DNA regions, Bioinformatics, vol.20, issue.18, pp.3413-3422, 2004.
DOI : 10.1093/bioinformatics/bth418

´. E. Lebarbier, Detecting multiple change-points in the mean of Gaussian process by model selection, Signal Processing, vol.85, issue.4, pp.717-736, 2005.
DOI : 10.1016/j.sigpro.2004.11.012
URL : https://hal.archives-ouvertes.fr/inria-00071847

G. Mclachlan and D. Peel, Finite mixture models, p.112, 2000.
DOI : 10.1002/0471721182

A. B. Olshen, E. Venkatraman, R. Lucito, and M. Wigler, Circular binary segmentation for the analysis of array-based DNA copy number data, Biostatistics, vol.5, issue.4, pp.557-572, 2004.
DOI : 10.1093/biostatistics/kxh008

F. Picard, S. Robin, M. Lavielle, C. Vaisse, and J. Daudin, A statistical approach for array CGH data analysis, BMC Bioinformatics, vol.6, issue.1, pp.27-109, 2005.
DOI : 10.1186/1471-2105-6-27
URL : https://hal.archives-ouvertes.fr/hal-01222433

S. Picard, E. Robin, J. Lebarbier, and . Daudin, A Segmentation/Clustering Model for the Analysis of Array CGH Data, Biometrics, vol.6, issue.3, pp.758-766, 2007.
DOI : 10.1111/j.1541-0420.2006.00729.x
URL : https://hal.archives-ouvertes.fr/hal-01197574

F. Ramírez and T. L. Davenport, Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010.
DOI : 10.1016/j.scienta.2010.06.024

G. Rigaill, E. Lebarbier, and S. Robin, Exact posterior distributions and model selection criteria for multiple change-point detection problems, Statistics and Computing, vol.63, issue.1, pp.917-929, 2012.
DOI : 10.1007/s11222-011-9258-8
URL : https://hal.archives-ouvertes.fr/hal-01000030

H. W. Watson and F. Galton, On the probability of the extinction of families. The Journal of the Anthropological Institute of Great Britain and Ireland, pp.138-144

N. R. Zhang and D. O. Siegmund, A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data, Biometrics, vol.6, issue.1, pp.22-32, 2007.
DOI : 10.1111/j.1541-0420.2006.00662.x

G. I. Allen and Z. Liu, A Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data, 2012 IEEE International Conference on Bioinformatics and Biomedicine, pp.1-6
DOI : 10.1109/BIBM.2012.6392619

S. Andersson, D. Madigan, and M. Perlman, Alternative Markov Properties for Chain Graphs, Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence, pp.40-48, 1996.
DOI : 10.1111/1467-9469.00224

D. Arbuckle, Python Testing: Beginner's Guide, 2010.

D. Bacciu, A. Micheli, and A. Sperduti, Bottom-Up Generative Modeling of Tree-Structured Data, Neural Information Processing. Theory and Algorithms, pp.660-668, 2010.
DOI : 10.1007/978-3-642-17537-4_80

O. Banerjee, L. Ghaoui, and A. Aspremont, Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data, The Journal of Machine Learning Research, vol.9, pp.485-516, 2008.

P. Barrett, J. Hunter, J. T. Miller, J. Hsu, and P. Greenfield, Matplotlib?a portable python plotting package, Astronomical Data Analysis Software and Systems XIV, p.91, 2005.

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

D. Barthélémy, C. Edelin, and F. Hallé, Architectural concepts for tropical trees In Tropical forests: botanical dynamics, speciation and diversity, pp.89-100, 1989.

J. Baudry, C. Maugis, and B. Michel, Slope heuristics: overview and implementation, Statistics and Computing, vol.6, issue.2, pp.455-470, 2012.
DOI : 10.1007/s11222-011-9236-1
URL : https://hal.archives-ouvertes.fr/hal-00461639

A. Bloesch, Aesthetic layout of generalized trees Software: Practice and Experience, pp.817-827, 1993.

C. Bron and J. Kerbosch, Algorithm 457: finding all cliques of an undirected graph, Communications of the ACM, vol.16, issue.9, pp.575-577, 1973.
DOI : 10.1145/362342.362367

C. Buchheim, M. Jünger, and S. Leipert, Improving Walker???s Algorithm to Run in Linear Time, Graph Drawing, pp.344-353, 2002.
DOI : 10.1007/3-540-36151-0_32

P. Bühlmann and A. J. Wyner, Variable length Markov chains. The Annals of Statistics, pp.480-513, 1999.

W. Buntine, Theory Refinement on Bayesian Networks, Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence, pp.52-60, 1991.
DOI : 10.1016/B978-1-55860-203-8.50010-3

W. Buntine, A guide to the literature on learning probabilistic networks from data, IEEE Transactions on Knowledge and Data Engineering, vol.8, issue.2, pp.195-210, 1996.
DOI : 10.1109/69.494161

F. Cazals and C. Karande, A note on the problem of reporting maximal cliques, Theoretical Computer Science, vol.407, issue.1-3, pp.564-568, 2008.
DOI : 10.1016/j.tcs.2008.05.010

E. Chacko, Physiology of vegetative and reproductive growth in mango (Mangifera indica L.) trees, Proceedings of the First Australian Mango Research Workshop, pp.54-70, 1986.

T. Chan, S. R. Kosaraju, M. T. Goodrich, and R. Tamassia, Optimizing area and aspect ratio in straight-line orthogonal tree drawings, Graph Drawing, pp.63-75, 1997.

D. Chickering, Learning equivalence classes of Bayesian-network structures, The Journal of Machine Learning Research, vol.2, pp.445-498, 2002.

D. Chickering, Optimal structure identification with greedy search, The Journal of Machine Learning Research, vol.3, pp.507-554, 2003.

H. Choi and R. G. Baraniuk, Multiscale image segmentation using wavelet-domain hidden Markov models, IEEE Transactions on Image Processing, vol.10, issue.9, pp.1309-1321, 2001.
DOI : 10.1109/83.941855

G. Claeskens and N. L. Hjort, Model selection and model averaging, 2008.

D. Cox and N. Wermuth, Linear Dependencies Represented by Chain Graphs, Statistical Science, vol.8, issue.3, pp.204-218, 1993.
DOI : 10.1214/ss/1177010887

I. Csiszár and Z. Talata, Context tree estimation for not necessarily finite memory processes , via bic and mdl. Information Theory, IEEE Transactions on, vol.52, issue.3, pp.1007-1016, 2006.

J. Dahl, V. Roychowdhury, and L. Vandenberghe, Maximum likelihood estimation of Gaussian graphical models: numerical implementation and topology selection, 2005.

A. Dambreville, Croissance et développement du manguier ( Mangifera indica L.) in natura: approche expérimentale et modélisation de l'influence d'un facteur exogène, la température, et de facteurs endogènes architecturaux, II-Sciences et Techniques du Languedoc, 2012.

A. Dambreville, P. Lauri, C. Trottier, Y. Guédon, and F. Normand, Deciphering structural and temporal interplays during the architectural development of mango trees, Journal of Experimental Botany, vol.64, issue.8, pp.2467-2480, 2013.
DOI : 10.1093/jxb/ert105
URL : https://hal.archives-ouvertes.fr/hal-00806978

N. Dasgupta, P. Runkle, L. Couchman, and L. Carin, Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data, Signal Processing, vol.81, issue.6, pp.1303-1316, 2001.
DOI : 10.1016/S0165-1684(00)00262-0

L. M. De-campos and J. M. Puerta, Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.228-2393, 2001.
DOI : 10.1007/3-540-44652-4_21

A. P. Dempster, Covariance Selection, Biometrics, vol.28, issue.1, pp.157-175, 1972.
DOI : 10.2307/2528966

L. Denoeud and A. Guénoche, Comparison of Distance Indices Between Partitions, Data Science and Classification, pp.21-28, 2006.
DOI : 10.1007/3-540-34416-0_3

M. Drton, Discrete chain graph models, Bernoulli, vol.15, issue.3, pp.736-753, 2009.
DOI : 10.3150/08-BEJ172

P. Eades, A heuristics for graph drawing, Congressus Numerantium, vol.42, pp.146-160, 1984.

P. Eades, Drawing free trees. International Institute for Advanced Study of Social Information Science, 1991.

D. Edwards, Introduction to graphical modelling, 2000.

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

R. Fernandez, Reconstruction tridimensionnelle et suivi de lignées cellulairesàcellulairesà partir d'images de microscopie laser: applicationàapplicationà des tissus végétaux, II-Sciences et Techniques du Languedoc, 2010.

R. Fernandez, P. Das, V. Mirabet, E. Moscardi, J. Traas et al., Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution, Nature Methods, vol.19, issue.7, pp.547-553, 2010.
DOI : 10.1038/nmeth.1472
URL : https://hal.archives-ouvertes.fr/hal-00521491

P. Ferraro and C. Godin, An Edit Distance between Quotiented Trees, Algorithmica, vol.36, issue.1, pp.1-39, 2003.
DOI : 10.1007/s00453-002-1002-5
URL : https://hal.archives-ouvertes.fr/hal-00307409

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.
DOI : 10.1093/biostatistics/kxm045

N. Friedman and M. Goldszmidt, Sequential update of Bayesian network structure, Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, pp.165-174, 1997.

T. M. Fruchterman and E. M. Reingold, Graph drawing by force-directed placement . Software: Practice and experience, pp.1129-1164, 1991.

M. Frydenberg, The chain graph Markov property, Scandinavian Journal of Statistics, vol.17, pp.333-353, 1990.

L. E. Gatsuk, O. V. Smirnova, L. I. Vorontzova, L. B. Zaugolnova, and L. A. Zhukova, Age States of Plants of Various Growth Forms: A Review, The Journal of Ecology, vol.68, issue.2, pp.675-696, 1980.
DOI : 10.2307/2259429

W. R. Gilks, Markov Chain Monte Carlo, 2005.
DOI : 10.1002/0470011815.b2a14021

G. Guennebaud and B. Jacob, Eigen v3, 2010.

P. Haccou, P. Jagers, and V. A. Vatutin, Branching processes: variation, growth, and extinction of populations, 2005.
DOI : 10.1017/CBO9780511629136

F. Hallé, R. A. Oldeman, and P. B. Tomlinson, Tropical trees and forests: an architectural analysis, 1978.
DOI : 10.1007/978-3-642-81190-6

T. E. Harris, The theory of branching processes, 2002.
DOI : 10.1007/978-3-642-51866-9

D. M. Hawkins, Point Estimation of the Parameters of Piecewise Regression Models, Applied Statistics, vol.25, issue.1, pp.51-57, 1976.
DOI : 10.2307/2346519

D. Heckerman, D. Geiger, and D. M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, pp.197-243, 1995.

J. D. Hunter, Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007.
DOI : 10.1109/MCSE.2007.55

F. V. Jensen, S. L. Lauritzen, and K. G. Olesen, Bayesian updating in causal probabilistic networks by local computations, Computational statistics quarterly, vol.4, pp.269-282, 1990.

N. Johnson, S. Kotz, and N. Balakrishnan, Discrete multivariate distributions, 1997.

E. Jones, T. Oliphant, and P. Peterson, Scipy: Open source scientific tools for python, 2001.

D. Karlis, An EM algorithm for multivariate Poisson distribution and related models, Journal of Applied Statistics, vol.30, issue.1, pp.63-77, 2003.
DOI : 10.1080/0266476022000018510

D. Karlis and L. Meligkotsidou, Multivariate Poisson regression with covariance structure, Statistics and Computing, vol.15, issue.4, pp.255-265, 2005.
DOI : 10.1007/s11222-005-4069-4

M. Kimmel and D. E. Axelrod, Branching processes in biology. interdisciplinary applied mathematics 19, 2002.

S. Kirkpatrick, D. G. Jr, and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

S. G. Kobourov, Spring embedders and force directed graph drawing algorithms, chapter 12, pp.383-408

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, 2009.

S. Lauritzen, Graphical models, 1996.

S. L. Lauritzen and N. Wermuth, Graphical models for associations between variables, some of which are qualitative and some quantitative. The Annals of Statistics, pp.31-57, 1989.

J. Legrand, Vers une compréhension multi-´ echelle du development floral: des réseaux auxiniques aux patrons de la dynamique cellulaire, 2014.

Z. Ma, X. Xie, and Z. Geng, Structural learning of chain graphs via decomposition, Journal of machine learning research: JMLR, vol.9, p.2847, 2008.

K. V. Mardia, Families of bivariate distributions, 1970.

P. Mccullagh and J. Nelder, Generalized linear models. Monographs on Statistics and Applied Probability 37, 1989.

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

G. Mclachlan and D. Peel, Finite mixture models, 2000.
DOI : 10.1002/0471721182

N. Meinshausen and P. Buhlmann, High-dimensional graphs and variable selection with the Lasso, The Annals of Statistics, vol.34, issue.3, pp.1436-1462, 2006.
DOI : 10.1214/009053606000000281

J. Moussouris, Gibbs and Markov random systems with constraints, Journal of Statistical Physics, vol.5, issue.13, pp.11-33, 1974.
DOI : 10.1007/BF01011714

R. E. Neapolitan, Learning Bayesian Networks, 2004.
DOI : 10.1016/B978-012370477-1.50021-9

W. Nultsch, Botanique générale, 1998.

F. Perez and B. E. Granger, IPython: A System for Interactive Scientific Computing, Computing in Science & Engineering, vol.9, issue.3, pp.21-29, 2007.
DOI : 10.1109/MCSE.2007.53

J. Peyhardi, Une nouvelle famille de modèles linéaires généralisés (GLMs) pour l'analyse de données catégorielles; applicationàapplicationà la structure et au développement des plantes, II-Sciences et Techniques du Languedoc, 2013.

J. Peyhardi, C. Trottier, and Y. Guédon, Partitioned conditional generalized linear models for categorical data. arXiv preprint arXiv:1405
URL : https://hal.archives-ouvertes.fr/hal-01101036

J. Peyhardi, C. Trottier, and Y. Guédon, A new specification of generalized linear models for categorical data
URL : https://hal.archives-ouvertes.fr/hal-00985595

F. Picard, S. Robin, M. Lavielle, C. Vaisse, and J. Daudin, A statistical approach for array CGH data analysis, BMC Bioinformatics, vol.6, issue.1, p.27, 2005.
DOI : 10.1186/1471-2105-6-27
URL : https://hal.archives-ouvertes.fr/hal-01222433

F. Picard, S. Robin, E. Lebarbier, and J. Daudin, A Segmentation/Clustering Model for the Analysis of Array CGH Data, Biometrics, vol.6, issue.3, pp.758-766, 2007.
DOI : 10.1111/j.1541-0420.2006.00729.x
URL : https://hal.archives-ouvertes.fr/hal-01197574

C. Pradal, S. Dufour-kowalski, F. Boudon, C. Fournier, and C. Godin, OpenAlea: a visual programming and component-based software platform for plant modelling, Functional Plant Biology, vol.35, issue.10, pp.751-760, 2008.
DOI : 10.1071/FP08084

R. Development and C. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2011.

A. E. Raftery, A model for high-order markov chains, Journal of the Royal Statistical Society. Series B (Methodological), pp.528-539, 1985.

F. Ramírez and T. L. Davenport, Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010.
DOI : 10.1016/j.scienta.2010.06.024

E. M. Reingold and J. Tilford, Tidier Drawings of Trees, IEEE Transactions on Software Engineering, vol.7, issue.2, pp.7223-228, 1981.
DOI : 10.1109/TSE.1981.234519

R. Robinson, Counting labeled acyclic digraphs, Combinatorial mathematics V, pp.28-43, 1973.

D. Ron, Y. Singer, and N. Tishby, The power of amnesia: Learning probabilistic automata with variable memory length, Machine Learning, vol.24, issue.1, pp.117-149, 1996.
DOI : 10.1007/BF00114008

D. J. Rose, R. E. Tarjan, and G. S. Lueker, Algorithmic Aspects of Vertex Elimination on Graphs, SIAM Journal on Computing, vol.5, issue.2, pp.266-283, 1976.
DOI : 10.1137/0205021

L. K. Saul and M. I. Jordan, Mixed memory Markov models: Decomposing complex stochastic processes as mixtures of simpler ones, Machine Learning, pp.75-87, 1999.

B. Schling, The boost C++ libraries, 2011.

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

D. R. Smyth, J. L. Bowman, and E. M. Meyerowitz, Early Flower Development in Arabidopsis, THE PLANT CELL ONLINE, vol.2, issue.8, pp.755-767, 1990.
DOI : 10.1105/tpc.2.8.755

T. Speed and H. Kiiveri, Gaussian Markov distributions over finite graphs. The Annals of Statistics, pp.138-150, 1986.

P. Spirtes, C. N. Glymour, and R. Scheines, Causation, prediction, and search, 2000.
DOI : 10.1007/978-1-4612-2748-9

B. Steinsky, Enumeration of labelled chain graphs and labelled essential directed acyclic graphs, Discrete Mathematics, vol.270, issue.1-3, pp.267-278, 2003.
DOI : 10.1016/S0012-365X(02)00838-5

K. Sugiyama and K. Misue, Graph Drawing by the Magnetic Spring Model, Journal of Visual Languages & Computing, vol.6, issue.3, pp.217-231, 1995.
DOI : 10.1006/jvlc.1995.1013

R. Tamassia, Handbook of Graph Drawing and Visualization (Discrete Mathematics and Its Applications), 2007.

R. Tarjan, Depth-First Search and Linear Graph Algorithms, SIAM Journal on Computing, vol.1, issue.2, pp.146-160, 1972.
DOI : 10.1137/0201010

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1, pp.267-288, 1996.

G. Tutz, Regression for categorical data, 2011.
DOI : 10.1017/CBO9780511842061

S. Van-der-walt, S. C. Colbert, and G. Varoquaux, The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, vol.13, issue.2, pp.22-30, 2011.
DOI : 10.1109/MCSE.2011.37
URL : https://hal.archives-ouvertes.fr/inria-00564007

J. Q. Walker, A node-positioning algorithm for general trees Software: Practice and Experience, pp.685-705, 1990.

H. W. Watson and F. Galton, On the Probability of the Extinction of Families., The Journal of the Anthropological Institute of Great Britain and Ireland, vol.4, pp.138-144
DOI : 10.2307/2841222

N. Wermuth, On block-recursive linear regression equations, Reuista Brasileim de Probabilidade Estattstic, vol.6, pp.1-56, 1992.

E. Yang, P. Ravikumar, G. Allen, and Z. Liu, Graphical models via generalized linear models, Advances in Neural Information Processing Systems 25, pp.1367-1375, 2012.

W. Yang, Some limit properties for Markov chains indexed by a homogeneous tree, Statistics & Probability Letters, vol.65, issue.3, pp.241-250, 2003.
DOI : 10.1016/j.spl.2003.04.001

M. Yannakakis, Computing the Minimum Fill-In is NP-Complete, SIAM Journal on Algebraic Discrete Methods, vol.2, issue.1, pp.77-79, 1981.
DOI : 10.1137/0602010