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SUMMARY:Message-passing inference on graphical models - Simon Byrne (Cambr
 idge)
DTSTART:20080715T103000Z
DTEND:20080715T113000Z
UID:TALK12664@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Graphical models provide a powerful framework for specifying c
 omplex relationships between random variables. However performing inferenc
 e on these models has remained a major challenge: exact inference is only 
 possible for the simplest of cases and Monte Carlo methods impose large co
 mputational requirements.\n\nAn alternative approach is using approximate 
 message-passing techniques. The most well known of these is the `loopy' ve
 rsion of belief propagation\, also known as the sum-product algorithm\, an
 d includes more recent developments such as generalised belief propagation
  (GBP) and expectation propagation (EP).\n\nThis talk will provide a brief
  summary of graphical models\, and an overview of belief propagation and r
 egion-based message-passing methods. The focus of the talk is to present t
 he concept of structured region graphs\, which incorporates the GBP and EP
  algorithms and provides a useful framework for developing more efficient 
 methods applicable to wider variety of problems.
LOCATION:Engineering Department\, CBL Room 438
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