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SUMMARY:Markov categories: towards a syntax for probability - Paolo Perron
 e (University of Oxford)
DTSTART:20210521T100000Z
DTEND:20210521T110000Z
UID:TALK160702@talks.cam.ac.uk
CONTACT:Nathanael Arkor
DESCRIPTION:Markov categories are a new categorical framework for treating
  randomness and information flow. \nThe basic question is: can we isolate 
 the fundamental axioms that are sufficient to prove the theorems of probab
 ility theory?\nThe traditional measure-theoretic approach to probability c
 an then be seen as a semantics for this theory\, possibly one out of many.
 \n\nSo far\, several theorems of probability have been proven in this synt
 hetic way: among them\, the de Finetti theorem and the zero-one laws of Ko
 lmogorov and Hewitt-Savage. \nIn 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 equa
 lity.\n\nThe latest preprint on the matter is https://arxiv.org/abs/2105.0
 2639\n\nJoint project with Tobias Fritz\, Tomas Gonda\, Dario Stein\, and 
 others.
LOCATION:https://meet.google.com/jxy-edcv-wgx
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