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SUMMARY:MCMC or Reservoir computing? A direct sampling approach
DTSTART;VALUE=DATE-TIME:20230630T090000Z
DTEND;VALUE=DATE-TIME:20230630T094000Z
DTSTAMP;VALUE=DATE-TIME:20260907T113839Z
UID:indico-contribution-214-1002@cern.ch
DESCRIPTION:Speakers: Petros Dellaportas (AUEB and UCL)\nAssume that we wo
 uld like to estimate the expected value of a function f with respect to a 
 density π by using an importance density function q. We prove that if π 
 and q are close enough under KL divergence\, an independent Metropolis sam
 pler estimator that obtains samplers from π with proposal density q\, enr
 iched with a variance reduction computational strategy based on control va
 riates\, achieves smaller asymptotic variance than the one from importance
  sampling. We illustrate our results in challenging option pricing problem
 s that require Monte Carlo estimation. Furthermore\, we propose an automat
 ic sampling methodology based on adaptive independent Metropolis that can 
 successfully reduce the asymptotic variance of an importance sampling esti
 mator and we demonstrate its applicability in a Bayesian inference problem
 s.\n\nhttps://indico.unina.it/event/67/contributions/1002/
LOCATION:Department of Political Sciences Aula Spinelli
URL:https://indico.unina.it/event/67/contributions/1002/
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