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PRODID:-//pretalx//pretalx.com//juliacon2021//speaker//BKDQGH
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UID:pretalx-juliacon2021-8BWJXP@pretalx.com
DTSTART:20210730T123000Z
DTEND:20210730T130000Z
DESCRIPTION:Calibrated probabilistic models ensure that predictions are con
 sistent with empirically observed outcomes\, and hence such models provide
  reliable uncertainty estimates for decision-making. This is particularly 
 important in safety-critical applications. We present Julia packages for a
 nalyzing calibration of general probabilistic predictive models\, beyond c
 ommonly studied classification models. Additionally\, our framework allows
  to perform statistical hypothesis testing of calibration.
DTSTAMP:20260413T020816Z
LOCATION:Green
SUMMARY:Calibration analysis of probabilistic models in Julia - David Widma
 nn
URL:https://pretalx.com/juliacon2021/talk/8BWJXP/
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