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SUMMARY:Calibration of probabilistic predictions - Johanna Ziegel (ETH Zü
 rich)
DTSTART:20250602T091500Z
DTEND:20250602T101500Z
UID:TALK232474@talks.cam.ac.uk
DESCRIPTION:Predictions for uncertain future outcomes should be calibrated
  in the sense that predicted probabilities for future events conform with 
 observed event frequencies. Probabilistic predictions take the form of pro
 bability distributions over all possible values of the future outcome. If 
 the future outcome is binary\, there is a broadly agreed notion of calibra
 tion for probabilistic predictions. However\, if the future outcome is mor
 e general\, such as real-valued or multivariate\, there are many notions o
 f calibration that have been proposed and are considered in forecast evalu
 ation. In this presentation\, different notions of calibration will be rev
 iewed alongside methodology to empirically assess calibration. Furthermore
 \, the connection of calibration to proper scoring rules will be discussed
 .
LOCATION:Seminar Room 1\, Newton Institute
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