Description du projet
Une méthode statistique améliorée d’analyse des données multivariées
Les expériences scientifiques modernes génèrent souvent des données qui requièrent l’analyse de plus de deux variables par observation. Le projet GRAPHMODE, financé par l’UE, prévoit de développer des méthodes efficaces d’analyse statistique de ces données. Pour leur étude, les chercheurs s’appuieront sur des modèles graphiques probabilistes qui expriment la structure de dépendance conditionnelle entre des variables aléatoires. Ces modèles constituent l’approche la plus avancée pour l’exploration détaillée des relations de cause à effet. Le projet mettra l’accent sur les questions algébriques et statistiques liées aux variables latentes (non mesurées) et aux boucles de rétroaction dans les modèles graphiques.
Objectif
Modern science increasingly relies on insights gained from sophisticated analyses of large data sets. An ambitious goal of such data-driven discovery is to understand complex systems via statistical analysis of multivariate data on the activity of their interacting units. Probabilistic graphical models, the topic of this project, are tailored to the task. The models facilitate refined yet tractable data exploration by using graphs to represent complex stochastic dependencies between considered variables. Models based on directed graphs, in particular, provide the state-of-the-art approach for detailed exploration of cause-effect relationships. However, modern applications of graphical models face numerous challenges such as key variables being latent (i.e. unobservable/unobserved), lacking temporal resolution in studies of feedback loops, and limited experimental interventions. Often arising in combination, these issues generally result in observed stochastic structure that cannot be characterized using the established notion of conditional independence. As a result, we are left with only a partial understanding of which aspects of a system can be inferred from the available data, and we lack effective methods for fundamental problems such as inference in the presence of feedback loops. The aim of the new project is to move beyond conditional independence structure to obtain a deeper understanding of the inherent limitations on what can be inferred from imperfect measurements, and to design novel statistical methodology to infer estimable quantities. The unique feature of the proposed work is a focus on algebraic relations among moments of probability distributions and the subtle statistical issues arising when such relations are to be exploited in practical methodology.
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Programme(s)
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Appel à propositions
(s’ouvre dans une nouvelle fenêtre) ERC-2019-ADG
Voir d’autres projets de cet appelRégime de financement
ERC-ADG - Advanced GrantInstitution d’accueil
80333 Muenchen
Allemagne