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SUMMARY:Sebastian Reich: Ensemble data assimilation: A coupling of measures
  perspective.
DESCRIPTION:Abstract:  Bayes’ formula provides the centerpiece for ensem
 ble data assimilation. However\, beyond its conceptional simplicity and be
 auty Bayes’ formula is hardly ever directly applicable and this is true 
 in particular when Bayes’ formula needs to be interfaced with complex sc
 ientific models. In this context it is better to talk of simulating Bayes
 ’ formula within a McKean perspective. Bayes’ formula can be simulated
  in the setting of sequential Monte Carlo methods and general Markov chain
  Monte Carlo methods. However\, while being unbiased these methods suffer 
 from the bias-variance dichotomy in high dimensions. In my talk\, I will s
 tart approaching Bayes’ formula from an entirely different perspective\;
  namely that of coupling probability measures and optimal transportation. 
 While this shift of focus in itself does not resolve the intractibility is
 sue of high dimensional systems\, it  naturally puts the popular ensemble
  Kalman filters into context and suggests natural extensions to non-Gaussi
 an data assimilation problems using linear programming which might lead to
  a better balance between bias and variance of the resulting estimators. 
URL:https://www.imperial.ac.uk/events/109828/sebastian-reich-ensemble-data-
 assimilation-a-coupling-of-measures-perspective/
DTSTART;TZID=Europe/London:20121009T150000
DTEND;TZID=Europe/London:20121009T160000
LOCATION:United Kingdom
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