ABSTRACT

This presentation will focus on diverse contributions related to statistical analysis of growth, branching and flowering patterns in plants. In a first part, I will present the statistical challenges raised in this context and illustrate how the notions of probabilistic graphical and hidden Markov models and multivariate counts can be relevant. In a second part, I will show how nonparametric Bayesian statistics can be used to tackle problems where the number of variables is a priori unknown and unbounded, as well as their potential benefits in some projects related to LiDAR data analysis. Eventually, I will mention several ongoing or forcasted projects to be conducted with colleagues in AMAP / AGAP / Cirad.

KEY WORDS

Hidden Markov models, Probabilistic graphical models, Statistical analysis of tree-structured data, Applications to plant structure analysis, Collaborations.