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SUMMARY:An analysis of implicit samplers in the small-noise limit - Kevin 
 Lin (University of Arizona)
DTSTART:20160615T140000Z
DTEND:20160615T150000Z
UID:TALK66452@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Weighted direct samplers\, also known as importance samplers\,
  are Monte Carlo algorithms for generating independent\, weighted samples 
 from a given target probability distribution.&nbsp\; Such algorithms have 
 a variety of applications in\, e.g.\, data assimilation\, state estimation
  for stochastic and chaotic dynamics\, and computational statistical mecha
 nics.&nbsp\; One challenge in designing and implementing weighted samplers
  is to ensure the variance of the weights\, and that of the resulting esti
 mator\, are well-behaved.&nbsp\; Recently\, Chorin\, Tu\, Morzfeld\, and c
 oworkers have introduced a class of novel weighted samplers called implcit
  samplers\, which have been shown to possess a number of nice properties.&
 nbsp\; In this talk\, I will report on an analysis of the variance of impl
 icit samplers in the small-noise limit and describe a simple method (sugge
 sted by the analysis) to obtain a higher-order implicit sampler.  Time per
 mitting\, I will also discuss how these methods can be applied to numerica
 l discretizations of SDEs.&nbsp\; This is joint work with Jonathan Goodman
 \, Andrew Leach\, and Matthias Morzfeld.
LOCATION:Seminar Room 2\, Newton Institute
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