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SUMMARY:Diffusion meets Nested Sampling - David Yallup (University of Camb
 ridge)
DTSTART:20240521T101500Z
DTEND:20240521T110000Z
UID:TALK217003@talks.cam.ac.uk
CONTACT:David Buscher
DESCRIPTION:Sampling techniques are a stalwart of reliable inference in th
 e physical sciences\, with the nested sampling paradigm emerging in the la
 st decade(s) as a ubiquitous tool for model fitting and comparison. Parall
 el developments in the field of generative machine learning have enabled a
 dvances in many applications of sampling methods in scientific inference p
 ipelines.\nThis work explores the synergy of the latest developments in di
 ffusion models and nested sampling. I will review the challenges of precis
 e model comparison in high dimension\, and explore how score based generat
 ive models can provide a solution. This work builds towards a public code 
 that can apply out of the box to many established hard problems in fundame
 ntal physics\, as well as providing potential to extend precise inference 
 to problems that are intractable with classical methods. I will motivate s
 ome potential applications at the frontiers of inference that can be unloc
 ked with these methods.
LOCATION:Coffee area\, Battcock Centre
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