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SUMMARY:Bayesian ERGMs -- computational and modelling challenges - Alberto
  Caimo (Dublin Institute of Technology)
DTSTART:20160728T081500Z
DTEND:20160728T091500Z
UID:TALK66901@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Recent research in statistical social network analysis has dem
 onstrated the advantages and effectiveness of Bayesian approaches to netwo
 rk data. In fact\, Bayesian exponential random graph models (BERGMs) are b
 ecoming increasingly popular as techniques for modelling relational data i
 n wide range of research areas. However\, the applicability of these model
 s in real-world settings is limited by computational complexity. In this s
 eminar we review some of the most recent computational methods for estimat
 ing BERGMs as well as extended ERGM-based modelling frameworks for dynamic
  and heterogenous social networks.
LOCATION:Seminar Room 2\, Newton Institute
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