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SUMMARY:John Baez (U.C. Riverside)
DTSTART:20220616T170000Z
DTEND:20220616T180000Z
DTSTAMP:20260423T003255Z
UID:MPML/80
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/80/">Sh
 annon Entropy from Category Theory</a>\nby John Baez (U.C. Riverside) as p
 art of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbst
 ract\nShannon entropy is a powerful concept. But what properties single ou
 t Shannon entropy as special? Instead of focusing on the entropy of a prob
 ability measure on a finite set\, it can help to focus on the "information
  loss"\, or change in entropy\, associated with a measure-preserving funct
 ion. Shannon entropy then gives the only concept of information loss that 
 is functorial\, convex-linear and continuous.\n\nThis is joint work with T
 om Leinster and Tobias Fritz.\n
LOCATION:https://researchseminars.org/talk/MPML/80/
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