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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Particle filters and curse of dimensionality - Reb
eschini\, P (Princeton University)
DTSTART;TZID=Europe/London:20140424T114000
DTEND;TZID=Europe/London:20140424T121500
UID:TALK52163AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/52163
DESCRIPTION:Co-author: Ramon van Handel (Princeton University)
\n\nA problem that arises in many applications is
to compute the conditional distributions of stoch
astic models given observed data. While exact comp
utations are rarely possible\, particle filtering
algorithms have proved to be very useful for appro
ximating such conditional distributions. Unfortuna
tely\, the approximation error of particle filters
grows exponentially with dimension\, a phenomenon
known as curse of dimensionality. This fact has r
endered particle filters of limited use in complex
data assimilation problems that arise\, for examp
le\, in weather forecasting. In this talk I will a
rgue that it is possible to develop local particle
filtering algorithms whose approximation error is
dimension-free. By exploiting conditional decay o
f correlations properties of high-dimensional mode
ls\, we prove for the simplest possible algorithm
of this type an error bound that is uniform both i
n time and in the model dimension. (Joint work wit
h R. van Handel)\n\nRelated Links: http://arxiv.or
g/abs/1301.6585 - Preprint \n\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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