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
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
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. ,
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
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
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
URL : http://arxiv.org/abs/1303.5709
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
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
Graph drawing by force-directed placement . Software: Practice and experience, pp.1129-1164, 1991. ,
The chain graph Markov property, Scandinavian Journal of Statistics, vol.17, pp.333-353, 1990. ,
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
Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, pp.197-243, 1995. ,
Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007. ,
DOI : 10.1109/MCSE.2007.55
Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983. ,
DOI : 10.1126/science.220.4598.671
Spring embedders and force directed graph drawing algorithms, chapter 12, pp.383-408 ,
Probabilistic graphical models: principles and techniques, p.35, 2009. ,
Graphical models for associations between variables, some of which are qualitative and some quantitative. The Annals of Statistics, pp.31-57, 1989. ,
Structural learning of chain graphs via decomposition, Journal of machine learning research: JMLR, vol.9, pp.2847-2887, 2008. ,
High-dimensional graphs and variable selection with the Lasso. The Annals of Statistics, pp.1436-1462, 2006. ,
Gibbs and Markov random systems with constraints, Journal of Statistical Physics, vol.5, issue.13, pp.11-33, 1974. ,
DOI : 10.1007/BF01011714
Learning Bayesian Networks, p.40, 2004. ,
DOI : 10.1016/B978-012370477-1.50021-9
Algorithmic Aspects of Vertex Elimination on Graphs, SIAM Journal on Computing, vol.5, issue.2, pp.266-283, 1976. ,
DOI : 10.1137/0205021
Gaussian Markov distributions over finite graphs. The Annals of Statistics, pp.138-150, 1986. ,
Causation, prediction, and search, p.40, 2000. ,
DOI : 10.1007/978-1-4612-2748-9
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
Handbook of Graph Drawing and Visualization (Discrete Mathematics and Its Applications) Chapman & Hall/CRC, p.13, 2007. ,
Depth-First Search and Linear Graph Algorithms, SIAM Journal on Computing, vol.1, issue.2, pp.146-160, 1972. ,
DOI : 10.1137/0201010
Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B, vol.58, issue.1, pp.267-288, 1996. ,
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
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
On block-recursive linear regression equations, Reuista Brasileim de Probabilidade Estattstic, vol.6, issue.38, pp.1-56, 1992. ,
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. ,
Graphical models via generalized linear models, Advances in Neural Information Processing Systems 25, pp.1367-1375, 2012. ,
Branching processes: variation, growth, and extinction of populations, p.60, 2005. ,
Tropical trees and forests: an architectural analysis, p.55, 1978. ,
DOI : 10.1007/978-3-642-81190-6
Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007. ,
DOI : 10.1109/MCSE.2007.55
Discrete multivariate distributions, p.61, 1997. ,
Branching processes in biology. interdisciplinary applied mathematics 19, p.60, 2002. ,
DOI : 10.1007/978-1-4939-1559-0
Vers une compréhension multi-´ echelle du development floral: des réseaux auxiniques aux patrons de la dynamique cellulaire, pp.2014-53 ,
Botanique générale, p.52, 1998. ,
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
Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010. ,
DOI : 10.1016/j.scienta.2010.06.024
Tidier Drawings of Trees, IEEE Transactions on Software Engineering, vol.7, issue.2, pp.7223-228, 1981. ,
DOI : 10.1109/TSE.1981.234519
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
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
Handbook of Graph Drawing and Visualization (Discrete Mathematics and Its Applications) Chapman & Hall/CRC, p.50, 2007. ,
A node-positioning algorithm for general trees Software: Practice and Experience, pp.685-705, 1990. ,
Bottom-up generative modeling of treestructured data, Neural Information Processing. Theory and Algorithms, pp.660-668, 2010. ,
A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The annals of Mathematical Statistics, pp.164-171, 1970. ,
Model selection and model averaging, p.75, 2008. ,
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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.320.2336
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
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
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
Hidden Markov processes, IEEE Transactions on Information Theory, vol.48, issue.6, pp.1518-1569, 2002. ,
DOI : 10.1109/TIT.2002.1003838
The theory of branching processes, p.71, 2002. ,
DOI : 10.1007/978-3-642-51866-9
Discrete multivariate distributions, p.85, 1997. ,
The EM algorithm and extensions, p.75, 2007. ,
Finite mixture models, p.73, 2004. ,
DOI : 10.1002/0471721182
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
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Early flower development in Arabidopsis . The Plant Cell Online, pp.755-767, 1990. ,
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
On the probability of the extinction of families. The Journal of the Anthropological Institute of Great Britain and Ireland, pp.138-144 ,
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
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 ,
Physiology of vegetative and reproductive growth in mango (Mangifera indica L.) trees, Proceedings of the First Australian Mango Research Workshop, pp.54-70, 1986. ,
Learning equivalence classes of Bayesian-network structures, The Journal of Machine Learning Research, vol.2, pp.445-498, 2002. ,
Optimal structure identification with greedy search, The Journal of Machine Learning Research, vol.3, pp.507-554, 2003. ,
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
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
Introduction to graphical modelling, p.90, 2000. ,
Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008. ,
DOI : 10.1093/biostatistics/kxm045
Markov Chain Monte Carlo, 2005. ,
DOI : 10.1002/0470011815.b2a14021
Branching processes: variation, growth, and extinction of populations, p.91, 2005. ,
DOI : 10.1017/CBO9780511629136
Univariate discrete distributions, p.95, 1993. ,
DOI : 10.1002/0471715816
Discrete multivariate distributions, pp.92-96, 1997. ,
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
Multivariate Poisson regression with covariance structure, Statistics and Computing, vol.15, issue.4, pp.255-265, 2005. ,
DOI : 10.1007/s11222-005-4069-4
Probabilistic graphical models: principles and techniques, p.96, 2009. ,
Structural learning of chain graphs via decomposition, Journal of machine learning research: JMLR, vol.9, pp.2847-90, 2008. ,
Families of bivariate distributions, p.92, 1970. ,
Generalized linear models. Monographs on Statistics and Applied Probability 37, p.94, 1989. ,
Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010. ,
DOI : 10.1016/j.scienta.2010.06.024
Counting labeled acyclic digraphs, Combinatorial mathematics V, pp.28-43, 1973. ,
DOI : 10.1007/bfb0069178
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
Graphical models via generalized linear models, Advances in Neural Information Processing Systems 25, pp.1367-1375, 2012. ,
Mixed graphical models via exponential families, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, pp.1042-1050, 2014. ,
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
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
Slope heuristics: overview and implementation, Statistics and Computing, vol.22, issue.2, pp.455-470, 2012. ,
Physiology of vegetative and reproductive growth in mango (Mangifera indica L.) trees, Proceedings of the First Australian Mango Research Workshop, pp.54-70, 1986. ,
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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.320.2336
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
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
Comparison of Distance Indices Between Partitions, Data Science and Classification, pp.21-28, 2006. ,
DOI : 10.1007/3-540-34416-0_3
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
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
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
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
Point Estimation of the Parameters of Piecewise Regression Models, Applied Statistics, vol.25, issue.1, pp.51-57, 1976. ,
DOI : 10.2307/2346519
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
Graphical models, p.110, 1996. ,
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
Finite mixture models, p.112, 2004. ,
DOI : 10.1002/0471721182
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
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-00427846
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
Mango (Mangifera indica L.) flowering physiology, Scientia Horticulturae, vol.126, issue.2, pp.65-72, 2010. ,
DOI : 10.1016/j.scienta.2010.06.024
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
On the probability of the extinction of families. The Journal of the Anthropological Institute of Great Britain and Ireland, pp.138-144 ,
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
Python Testing: Beginner's Guide, p.127, 2010. ,
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
Variable length Markov chains. The Annals of Statistics, pp.480-513, 1999. ,
Context tree estimation for not necessarily finite memory processes , via bic and mdl. Information Theory, IEEE Transactions on, vol.52, issue.127, pp.1007-1016, 2006. ,
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
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
Introduction to graphical modelling, p.125, 2000. ,
Eigen v3, p.127, 2010. ,
Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007. ,
DOI : 10.1109/MCSE.2007.55
Bayesian updating in causal probabilistic networks by local computations, Computational statistics quarterly, vol.4, pp.269-282, 1990. ,
Scipy: Open source scientific tools for python, p.127, 2001. ,
Structural learning of chain graphs via decomposition, Journal of machine learning research: JMLR, vol.9, pp.2847-125, 2008. ,
Finite mixture models, p.131, 2004. ,
DOI : 10.1002/0471721182
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
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, p.125, 2013. ,
Partitioned conditional generalized linear models for categorical data. arXiv preprint arXiv:1405 ,
URL : https://hal.archives-ouvertes.fr/hal-01101036
A new specification of generalized linear models for categorical data ,
URL : https://hal.archives-ouvertes.fr/hal-00985595
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: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2011. ,
A model for high-order markov chains, Journal of the Royal Statistical Society. Series B (Methodological), vol.127, pp.528-539, 1985. ,
The power of amnesia: Learning probabilistic automata with variable memory length, Machine Learning, vol.24, issue.1, pp.117-1491026490906255, 1996. ,
DOI : 10.1007/BF00114008
Mixed memory Markov models: Decomposing complex stochastic processes as mixtures of simpler ones, Machine Learning, pp.75-87, 1999. ,
The boost C++ libraries, p.127, 2011. ,
Regression for categorical data, p.129, 2011. ,
DOI : 10.1017/CBO9780511842061
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
Graphical models via generalized linear models, Advances in Neural Information Processing Systems 25, pp.1367-1375, 2012. ,
Mixed graphical models via exponential families, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics Index of references, pp.1042-1050, 2014. ,