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Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models

Pierre Fernique 1, 2 Jean-Baptiste Durand 3, 1, * Yann Guédon 1, 2
* Corresponding author
1 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, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and ecology (where the data can represent, for example, children counts associated with multitype branching processes), sociology and econometrics. We focus on I) Identifying categories that appear simultaneously, or on the contrary that are mutually exclusive. This is achieved by identifying conditional independence relationships between the variables; II)Building parsimonious parametric models consistent with these relationships; III) Characterising and testing the effects of covariates on the joint distribution of the counts. To achieve these goals, we propose an approach based on graphical probabilistic models, and more specifically partially directed acyclic graphs.
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Submitted on : Friday, December 13, 2013 - 5:43:48 PM
Last modification on : Thursday, March 24, 2022 - 3:37:08 AM
Long-term archiving on: : Tuesday, March 18, 2014 - 12:55:40 PM


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  • HAL Id : hal-00918597, version 1
  • ARXIV : 1312.4479


Pierre Fernique, Jean-Baptiste Durand, Yann Guédon. Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models. AIGM13 - 3rd Workshop on Algorithmic issues for Inference in Graphical Models, Sep 2013, Paris, France. ⟨hal-00918597⟩



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