Random Structures and Patterns in Spatio-Temporal Data: Probabilistic Modelling and Statistical Inference
When and Where
Speakers
Description
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.
About Radu Stoica
Radu S. Stoica is full professor in mathematics at University of Lorraine (France). His research activity connects stochastic geometry and spatial statistics for probabilistic modeling and statistical description of random structures and patterns. The results of his work consist of tailored to the data methodologies based on Markov models, Monte Carlo algorithms and inference procedures, that are able to characterize and detect structures and patterns hidden in the data. The tackled application domains are: astronomy, geosciences, image analysis and network sciences. Previously to his current position, Radu Stoica was associate professor at University of Lille (France). He was also worked as a researcher for INRAe Avignon (France), University Jaume I (Spain) and CWI Amsterdam (The Netherlands).