Random structures and patterns in spatio-temporal data: probabilistic modelling and statistical inference

Radu S. Stoica
University of Lorraine

The useful information carried by spatio-temporal data is often outlined by geometric structures and patterns. Filaments or clusters induced by galaxy positions in our Universe are such an example.

Two situations are to be considered. First, the pattern of interest is hidden in the data set, hence the pattern should be detected. Second, the structure to be studied is observed, so relevant characterization of it should be done.

Probabilistic modelling is one of the approaches that allows to furnish answers to these questions. This is done by developing unitary methodologies embracing simultaneously three directions: modelling, simulation and inference.

This talk presents the use of marked point processes applied to such structures detection and characterization. Practical examples are also shown.


Radu S. Stoica is professor in mathematics at University of Lorraine. His research interests are related to probabilistic modeling and statistical inference for pattern analysis within spatio-temporal data. His theoretical work proposes adapted Markov models, Monte Carlo algorithms and inference procedures able to characterize and detect structured patterns hidden in the data. The application domains aimed are: astronomy, geosciences, image analysis and network sciences.