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SUMMARY:Nonparametrics for networks - Sofia Olhede (University College Lon
 don)
DTSTART:20160714T100000Z
DTEND:20160714T103000Z
UID:TALK67205@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Relational data have become an important component of mo
 dern statistics. Networks\, and weighted networks\, are ubiquitous in mode
 rn applications such as disease dynamics\, ecology\, financial contagion\,
  and neuroscience. The inference of networks is harder\, in parts because 
 the measure placed on the observables need to satisfy sets of permutation 
 invariances\, and most networks are very sparse\, with most possible relat
 ions not present.<br><br> This talk will explore how to best construct non
 parametric summaries of such objects\, in such as way that the underlying 
 statistical model of the observations is well described\, and any estimato
 rs computable with scalable algorithms.</span>  <br><span><br>This is join
 t work with Pierre-Andre Maugis and Patrick Wolfe (UCL).</span>
LOCATION:Seminar Room 1\, Newton Institute
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