Watch on YouTube
Possibilistic Inference - From Imprecise Probabilities to Inferential Models
Possibility theory provides us with a useful language for describing various kinds of uncertainty. It serves not only as a mathematical description of imprecise probabilities, but it also constitutes a very natural approach to statistical inference in the framework of inferential models proposed by Martin and Liu. This talk argues in favor of these claims, it highlights the connections between the two proposed applications of possibility theory, and it demonstrates how possibilistic inferential models essentially provide the tools for efficiently computing with nested confidence intervals.
Dominik Hose received his B.Sc. and M.Sc. in Simulation Technology from the University of Stuttgart (Germany) in 2015 and 2017, respectively. For the last five years, he has been a PhD student working on possibilistic uncertainty quantification with imprecise probabilities under the supervision of Michael Hanss. In January 2022, he submitted his dissertation entitled "Possibilistic Reasoning with Imprecise Probabilities: Statistical Inference and Dynamic Filtering".