ELLIS Delft Research theme - Bayesian, probabilistic, statistical & causal Machine Learning

Topics: Uncertainty quantification, probabilistic models, causal models

Using Bayesian reasoning within machine learning allows us to reason in terms of statistics, probabilities, uncertainties, and causality. This theme brings together researchers from theoretical disciplines, focusing e.g. on computational efficiency, decision-making, causal inference and supervised learning, as well as researchers from the application fields of e.g. perception for intelligent vehicles, visual data for architectural design, computational molecular biomedicine, socially aware surveillance systems, and sensor fusion.

Theme Coordinator

Manon Kok

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