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SUMMARY:Sequential Monte Carlo with Highly Informative Observations - Murr
 ay\, L (CSIRO)
DTSTART:20140425T101500Z
DTEND:20140425T105000Z
UID:TALK52183@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-author: Pierre Del Moral (University of New South Wales)\n\
 nWe introduce a sequential Monte Carlo (SMC) method for sampling the state
  of continuous-time state-space models when observations are highly inform
 ative\, a situation in which standard SMC methods can perform poorly. The 
 most extreme case is where the observations are exact---of the state itsel
 f---and the problem is that of simulating diffusion bridges between given 
 starting and ending states. The basic idea is to introduce a sequence of i
 ntermediate weighting and resampling steps between observation times\, gui
 ding particles towards the ending state. A few designs that have been usef
 ul in practice are given\, and demonstrated on some applied problems that 
 feature complex models.\n
LOCATION:Seminar Room 1\, Newton Institute
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