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SUMMARY:Bayesian Workflow - Andrew Gelman (Columbia University)
DTSTART:20250626T143000Z
DTEND:20250626T153000Z
UID:TALK232375@talks.cam.ac.uk
DESCRIPTION:The workflow of applied Bayesian statistics includes not just 
 inference but also building\, checking\, and understanding fitted models. 
 We discuss various live issues including prior distributions\, data models
 \, and computation\, in the context of ideas such as the Fail Fast Princip
 le and the Folk Theorem of Statistical Computing. We also consider some ex
 amples of Bayesian models that give bad answers and see if we can develop 
 a workflow that catches such problems. For background\, see here: http://w
 ww.stat.columbia.edu/~gelman/research/unpublished/Bayesian_Workflow_articl
 e.pdf
LOCATION:Seminar Room 1\, Newton Institute
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