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A statistical modeling framework for analyzing tree-indexed data: Application to plant development on microscopic and macroscopic scales

Pierre Fernique 1, 2
1 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales, INRA - Institut National de la Recherche Agronomique, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We address statistical models for tree-indexed data.In Virtual Plants team, the host team for this thesis, applications of interest focus on plant development and its modulation by environmental and genetic factors.We thus focus on plant developmental applications both at a microscopic level with the study of the cell lineage in the biological tissue responsible for the plant growth, and at a macroscopic level with the mechanism of branch production.Far fewer models are available for tree-indexed data than for path-indexed data.This thesis therefore aims to propose a statistical modeling framework for studying patterns in tree-indexed data.To this end, two different classes of statistical models, Markov and change-point models, are investigated
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  • HAL Id : tel-01095420, version 1

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Pierre Fernique. A statistical modeling framework for analyzing tree-indexed data: Application to plant development on microscopic and macroscopic scales. Statistics [math.ST]. Universite de Montpellier 2, 2014. English. ⟨tel-01095420⟩

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