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SUMMARY:Two network scale challenges:Constructing and fitting hierarchical
  block models and fitting large block models using the mean field method -
  Peter Bickel (University of California\, Berkeley)
DTSTART:20180626T104500Z
DTEND:20180626T113000Z
UID:TALK107401@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Work with S.Bhattacharyya\,T.Li\,E.Levina\,S.Mukherjee\,P.Sark
 ar  Networks are a complex type of structure presenting itself in many app
 lications . They are usually represented by a graph \,with possibly weight
 ed edges plus additional covariates (such as directions).Block models have
  been studied for some time as basic approximations to ergodic stationary 
 probability models for single graphs.A huge number of fitting methods have
  been developed for these models some of which we will touch on.  The mean
  field method in which an increasing number of parameters must be fitted i
 s used not only for multiple membership block models but also in applicati
 ons such as LDA.if the graph is too large poor behaviour of the method can
  be seen.. We have developed what we call "patch methods " for fitting whi
 ch help both computationally and inferentially in such situations bur much
  further analysis is needed.  It is intuitively clear but mathematically u
 nclear how knowledge of the model having nested scales helps in fitting la
 rge scale as opposed to small scale parameters.We will discuss this issue 
 through an example\,
LOCATION:Seminar Room 1\, Newton Institute
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