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SUMMARY:Particle filters for infinite-dimensional systems: combining local
 ization and optimal transportation - Reich\, S (Universitt Potsdam)
DTSTART:20140422T132000Z
DTEND:20140422T135500Z
UID:TALK52086@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-author: Yuan Cheng (University of Potsdam) \n\nParticle fil
 ters or sequential Monte Carlo methods are powerful tools for adjusting mo
 del state to data. However they suffer from the curse of dimensionality an
 d have not yet found wide-spread application in the context of spatio-temp
 oral evolution models. On the other hand\, the ensemble Kalman filter with
  its simple Gaussian approximation has successfully been applied to such m
 odels using the concept of localization. Localization allows one to accoun
 t for a spatial decay of correlation in a filter algorithm. In my talk\, I
  will propose novel particle filter implementations which are suitable for
  localization and\, as the ensemble Kalman filter\, fit into the broad cla
 ss of linear transform filters. In case of a particle filter this transfor
 mation will be determined by ideas from optimal transportation while in ca
 se of the ensemble Kalman filter one essentially relies on the linear Kalm
 an update formulas. This common framework also allows for a mixture of par
 ticle and ensemble Kalman filters. Numerical results will be provided for 
 the Lorenz-96 model which is a crude model for nonlinear advection. \n
LOCATION:Seminar Room 1\, Newton Institute
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