Projektbeschreibung
Ein verbessertes multivariates statistisches Analyseverfahren
Moderne wissenschaftliche Experimente ergeben häufig Daten, deren Analyse mehr als zwei Variablen pro Beobachtung erfordert. Ziel des EU-finanzierten Projekts GRAPHMODE ist die Entwicklung effizienter Verfahren zur statistischen Analyse solcher Daten. Für ihre Studie werden die Forschenden auf probabilistische graphische Modelle zur Darstellung der bedingten Abhängigkeitsstruktur zwischen Zufallsvariablen zurückgreifen. Diese Modelle stellen den modernsten Ansatz für die eingehende Untersuchung von Kausalzusammenhängen dar. Das Projekt wird einen besonderen Schwerpunkt auf algebraische und statistische Fragen im Zusammenhang mit latenten (nicht gemessenen) Variablen und Rückkopplungsschleifen in grafischen Modellen legen.
Ziel
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.
Schlüsselbegriffe
Programm/Programme
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Aufforderung zur Vorschlagseinreichung
(öffnet in neuem Fenster) ERC-2019-ADG
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
ERC-ADG - Advanced GrantGastgebende Einrichtung
80333 Muenchen
Deutschland