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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:An analysis of implicit samplers in the small-nois
e limit - Kevin Lin (University of Arizona)
DTSTART;TZID=Europe/London:20160615T150000
DTEND;TZID=Europe/London:20160615T160000
UID:TALK66452AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/66452
DESCRIPTION:Weighted direct samplers\, also known as importanc
e samplers\, are Monte Carlo algorithms for genera
ting independent\, weighted samples from a given t
arget probability distribution. \; Such algori
thms have a variety of applications in\, e.g.\, da
ta assimilation\, state estimation for stochastic
and chaotic dynamics\, and computational statistic
al mechanics. \; One challenge in designing an
d implementing weighted samplers is to ensure the
variance of the weights\, and that of the resultin
g estimator\, are well-behaved. \; Recently\,
Chorin\, Tu\, Morzfeld\, and coworkers have introd
uced a class of novel weighted samplers called imp
lcit samplers\, which have been shown to possess a
number of nice properties. \; In this talk\,
I will report on an analysis of the variance of im
plicit samplers in the small-noise limit and descr
ibe a simple method (suggested by the analysis) to
obtain a higher-order implicit sampler. Time per
mitting\, I will also discuss how these methods ca
n be applied to numerical discretizations of SDEs.
\; This is joint work with Jonathan Goodman\,
Andrew Leach\, and Matthias Morzfeld.
LOCATION:Seminar Room 2\, Newton Institute
CONTACT:INI IT
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