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SUMMARY:Local Sequential Monte Carlo Methods - Dr Adam Johansen\, Statisti
 cs Department\, University of Warwick
DTSTART:20111026T131500Z
DTEND:20111026T140000Z
UID:TALK32615@talks.cam.ac.uk
CONTACT:Rachel Fogg
DESCRIPTION:Sequential Monte Carlo methods (often termed particle filters 
 in this context) are one of the most versatile computational approaches to
  the (discrete time) filtering problem. The work presented develops techni
 ques which allow almost automatic block-sampling in this setting. This app
 roach substantially improving the path-space performance of these algorith
 ms\, allowing online inference in settings in which the whole trajectory o
 f the unobserved Markov process is of interest. Results for simple example
 s illustrate the potential of the proposed approach.
LOCATION:LR4\, Engineering\, Department of
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