On the Intersection and Composition properties for discrete random variables.

Abstract: Compositional graphoids are fundamental combinatorial structures in the field of causality where they usually appear disguised as graphical models. Their usefulness relies on the Intersection and Composition properties. We survey sufficient conditions for a system of discrete random variables to satisfy either of these properties and tie this investigation to conditional information inequalities.

Computer scienceMathematics

Audience: researchers in the discipline

Seminar on Algorithmic Aspects of Information Theory

Series comments: This online seminar is a follow up of the Dagstuhl Seminar 22301, www.dagstuhl.de/en/program/calendar/semhp/?semnr=22301.