Estimation of Discrete Partially Directed Acyclic Graphical Models in Multitype Branching Processes

Pierre Fernique 1, 2 Jean-Baptiste Durand 2, 3 Yann Guédon 1, 2
2 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We address the inference of discrete-state models for tree-structured data. Our aim is to introduce parametric multitype branching processes that can be efficiently estimated on the basis of data of limited size. Each generation distribution within this macroscopic model is modeled by a partially directed acyclic graphical model. The estimation of each graphical model relies on a greedy algorithm for graph selection. We present an algorithm for discrete graphical model which is applied on multivariate count data. The proposed modeling approach is illustrated on plant architecture datasets.
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Pierre Fernique, Jean-Baptiste Durand, Yann Guédon. Estimation of Discrete Partially Directed Acyclic Graphical Models in Multitype Branching Processes. COMPSTAT 2014, 21st International Conference on Computational Statistics, The International Association for Statistical Computing (IASC), Aug 2014, Geneva, Switzerland. pp.561-568. ⟨hal-01084524⟩

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