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SUMMARY:Importance Sampling with Particle Flows - Dr Pete Bunch\, Cambridg
 e University
DTSTART:20141106T140000Z
DTEND:20141106T150000Z
UID:TALK55634@talks.cam.ac.uk
CONTACT:Fredrik Lindsten
DESCRIPTION:The Bayesian approach to inference is based upon calculating p
 osterior probability distributions. When these are not analytically tracta
 ble\, we can instead estimate lots of useful properties by sampling. Howev
 er\, drawing samples from a posterior distribution is also a challenging t
 ask. This talk will focus on two methods for achieving this\, importance s
 ampling and particle flow sampling.\n\nImportance sampling is a well-estab
 lished\, well-used\, well-studied algorithm\, in which samples are drawn f
 rom an importance distribution\, and then weighted so as to represent the 
 posterior. Particle flow sampling instead uses a differential equation to 
 move samples through the state space to positions that represent the poste
 rior.\n\nWe discuss the relative advantages and disadvantages of these two
  methods\, and show how they may be usefully combined to achieve better in
 ference with a class of nonlinear Gaussian models.
LOCATION:BN3-05 (Sigproc Meeting Room)\, 3rd Floor of CUED
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