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SUMMARY:Data assimilation: A dynamic homotopy-based coupling approach - Se
 bastian Reich (Universität Potsdam)
DTSTART:20220922T100000Z
DTEND:20220922T102000Z
UID:TALK179048@talks.cam.ac.uk
DESCRIPTION:Homotopy approaches to Bayesian inference have found widesprea
 d use especially if the Kullback-Leibler divergence between the prior and 
 the posterior distribution is large. Here we extend one of these homotopy 
 approach to include an underlying stochastic diffusion process. The underl
 ying mathematical problem is closely related to the Schr&ouml\;dinger brid
 ge problem for given marginal distributions. We demonstrate that the propo
 sed homotopy approach provides a computationally tractable approximation t
 o the underlying bridge problem. In particular\, our implementation builds
  upon the widely used ensemble Kalman filter methodology and extends it to
  Schr&ouml\;dinger bridge problems within the context of sequential data a
 ssimilation. &nbsp\;&nbsp\;
LOCATION:No Room Required
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