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UID:pretalx-pydata-london-2026-JL7YAJ@pretalx.com
DTSTART;TZID=GMT:20260605T160000
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DESCRIPTION:Posterior predictive checks are a key step within Bayesian mode
 ling workflows where we compare model predictions with the data used to fi
 t the model. By focusing on distributional comparisons instead of point es
 timates\, they offer valuable insights about our models\, where they fail 
 and inform model improvements. _Knowing a model is not completely right is
  relatively easy_\, knowing **why** that is the case and **how to fix it**
  are a whole other question which will be the focus of the tutorial. This 
 tutorial will provide data scientists and researchers with multiple strate
 gies for posterior predictive checks to allow their use in continuous\, di
 screte or categorical data\, and for homogeneous or heterogeneous data.
DTSTAMP:20260602T225406Z
LOCATION:Hardwick Hub
SUMMARY:Model criticism through posterior predictive checks - Oriol Abril P
 la
URL:https://pretalx.com/pydata-london-2026/talk/JL7YAJ/
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