Categorical information geometry - Perrone, P (Springer, 2023)
Information geometry is the study of interactions between random variables by means of metric, divergences, and their geometry. Categorical probability has a similar aim, but uses algebraic structures, primarily monoidal categories, for that purpose. As recent work shows, we can unify the two approaches by means of enriched category theory into a single formalism, and recover important information-theoretic quantities and results, such as entropy and data processing inequalities.