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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20250220T092450Z
DTSTART:20250305T150000Z
DTEND:20250305T160000Z
SUMMARY:SQUIDS Seminar - Nested expectations with kernel quadrature
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}p1q0-m7d4yt
 l3-34igx2
DESCRIPTION:This paper considers the challenging computational task of es
 timating nested expectations. Existing algorithms\, such as nested Monte
  Carlo or multilevel Monte Carlo\, are known to be consistent but requir
 e a large number of samples at both inner and outer levels to converge. 
 Instead\, we propose a novel estimator consisting of nested kernel quadr
 ature estimators and we prove that it has a faster convergence rate than
  all baseline methods when the integrands have sufficient smoothness. We
  then demonstrate empirically that our proposed method does indeed requi
 re fewer  samples to estimate nested expectations on real-world applicat
 ions including Bayesian optimisation\, option pricing\, and health econo
 mics.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:G.113\, Alan Turing Building\, Manchester
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