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SUMMARY:Scoring rules and their approximations on manifolds - Karthik  Bha
 rath (University of Nottingham)
DTSTART:20250606T111500Z
DTEND:20250606T114500Z
UID:TALK232171@talks.cam.ac.uk
DESCRIPTION:On metric spaces of strong negative type an energy or kernel-b
 ased strictly proper scoring rule for probabilistic forecasts may be defin
 ed. However\, the relationship between the strong negative type property a
 nd the curvature of a metric space that is a manifold is not well understo
 od. I will comment on this issue while drawing parallels to conditions on 
 the curvature that determine efficient sampling on manifolds using intrins
 ic stochastic differential equations (SDEs). I will then discuss error bou
 nds for SDE-based sampling from forecasts distributions on manifolds\, and
  their application to computing the corresponding scoring rules.&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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