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SUMMARY:A* Sampling - Chris Maddison  (U Toronto)
DTSTART:20150224T110000Z
DTEND:20150224T120000Z
UID:TALK58164@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:The problem of drawing samples from a discrete distribution ca
 n be converted into a discrete optimization problem. In this work\, we sho
 w how sampling from a continuous distribution can be converted into an opt
 imization problem over continuous space. Central to the method is a stocha
 stic process recently described in mathematical statistics that we call th
 e Gumbel process. We present a new construction of the Gumbel process and 
 A-star sampling\, a practical generic sampling algorithm that searches for
  the maximum of a Gumbel process using A-star search. We analyze the corre
 ctness and convergence time of A-star sampling and demonstrate empirically
  that it makes more efficient use of bound and likelihood evaluations than
  the most closely related adaptive rejection sampling-based algorithms.
LOCATION:Engineering Department\, CBL Room BE-438.
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