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Markov categories: towards a syntax for probability

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If you have a question about this talk, please contact Nathanael Arkor.

Markov categories are a new categorical framework for treating randomness and information flow. The basic question is: can we isolate the fundamental axioms that are sufficient to prove the theorems of probability theory? The traditional measure-theoretic approach to probability can then be seen as a semantics for this theory, possibly one out of many.

So far, several theorems of probability have been proven in this synthetic way: among them, the de Finetti theorem and the zero-one laws of Kolmogorov and Hewitt-Savage. In addition, along the way, a lot of deep concepts of probability have been given an elegant categorical description, such as the concepts of stochastic independence and of almost-sure equality.

The latest preprint on the matter is

Joint project with Tobias Fritz, Tomas Gonda, Dario Stein, and others.

This talk is part of the Logic & Semantics for Dummies series.

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