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SUMMARY:Bayesian Methods for Networks - Peter Hoff (University of Washingt
 on)
DTSTART:20160725T090000Z
DTEND:20160725T100000Z
UID:TALK66836@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Statistical analysis of social network data presents many chal
 lenges:  Realistic models often require a large number of parameters\, yet
  maximum  likelihood estimates for even the simplest models may be unstabl
 e. Furthermore\,  network data often exhibit non-standard statistical depe
 ndencies\, and most  network datasets lack any sort of replication. <br> <
 span><br>Statistical methods to address these issues have included random 
 effects and  latent variable models\, and penalized likelihood methods. In
  this talk I will  discuss how these approaches fit naturally within a Bay
 esian framework for  network modeling. Additionally\, we will discuss how 
 standard Bayesian concepts  such as exchangeability play a role in the dev
 elopment and interpretation of  probability models for networks. Finally\,
  some thoughts on the use of Bayesian  methods for large-scale dynamic net
 works will be presented.</span>
LOCATION:Seminar Room 1\, Newton Institute
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