Abstract
Many alternatives to the probabilistic modelling of information have been proposed since the birth of modern Statistics; yet, few have been successfully applied to the complex inference problems that modern Statisticians are faced with. In this talk, I will introduce a specific combination of probability theory and possibility theory which allows for modelling both randomness and (deterministic) information. I will explore some of the practical advantages of such an approach and show that this framework can be applied to complex systems through applications to space-debris tracking and data assimilation.
Brief Biography
Jeremie Houssineau is an assistant professor in the Department of Statistics at the University of Warwick. His research interests include possibility theory and Bayesian statistics. In particular, representation of uncertainty, multi-target tracking and multilevel and multi-index Monte Carlo methods. Houssineau received a Ph.D. in statistical signal processing from Heriot-Watt University, Edinburgh, in 2015 and was with the Department of Statistics and Applied Probability at the National University of Singapore from 2016 to 2019.