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SUMMARY:Sequential Monte Carlo Samplers - Nick Whiteley
DTSTART:20061201T153000Z
DTEND:20061201T170000Z
UID:TALK5802@talks.cam.ac.uk
CONTACT:Taylan Cemgil
DESCRIPTION:\nThe particle filter is a recursive\, importance sampling-bas
 ed scheme which yields weighted sample approximations to a sequence of pos
 terior probability distributions on spaces of increasing dimension. Sequen
 tial Monte Carlo Samplers are a very general class of algorithms which ena
 ble the same task to be performed for sequences of distributions which nee
 d not be defined on spaces of increasing dimension. \n\nThis talk will sum
 marise importance sampling\, the particle filter and go on to describe the
  Sequential Monte Carlo Samplers framework\, pointing out relationships to
  other existing inference alogrithms. A sketch will be given of an applica
 tion to an audio signal processing problem involving trans-dimensional sta
 te spaces.\n\n"Sequential Monte Carlo Samplers"\, (with P. Del Moral & A.\
 nJasra)\, /J. Royal Statist. Soc. /B\, vol. 68\, no. 3\, pp. 411-436\,\n20
 06.\n\nhttp://www.cs.ubc.ca/~arnaud/delmoral_doucet_jasra_sequentialmontec
 arlosamplersJRSSB.pdf\n\n\n
LOCATION:Engineering Department\, Baker Building\, Division F meeting room
 \, 5th floor
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