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SUMMARY:Approximation strategies for structure learning in Bayesian networ
 ks  - Teppo Niinimäki (Helsinki)
DTSTART:20160607T083000Z
DTEND:20160607T093000Z
UID:TALK66503@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Structure discovery in Bayesian networks has attracted conside
 rable interest in the recent decades. Attention has mostly been paid to fi
 nding a structure that best fits the data under certain criterion. The opt
 imization approach can lead to noisy and partly arbitrary results due to t
 he uncertainty caused by a small amount of data. The so-called full Bayesi
 an approach addresses this shortcoming by learning the posterior distribut
 ion of structures. In practice\, the posterior distribution is summarized 
 by constructing a representative sample of structures\, or by computing ma
 rginal posterior probabilities of individual arcs or other substructures. 
 The state-of-the-art sampling algorithms draw orderings of variables along
  a Markov chain. We have proposed several improvements to these algorithms
 . In this talk I discuss these improvements. 
LOCATION:Engineering Department\, CBL Room BE-438
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