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SUMMARY:Gesine Reinert (Oxford University)
DTSTART:20201015T070000Z
DTEND:20201015T080000Z
DTSTAMP:20260423T040047Z
UID:PVSeminar/5
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/PVSeminar/5/
 ">Stein’s method for multivariate distributions\, kernelized goodness of
  fit statistics\, and exponential random graph models</a>\nby Gesine Reine
 rt (Oxford University) as part of Probability Victoria Seminar (PVSeminar)
 \n\n\nAbstract\nAssessing the goodness of fit of models with continuous di
 stributions for which the likelihood cannot be evaluated directly can be t
 ackled using kernels which are based on Stein’s method for continuous mu
 ltivariate distributions. Often independent replicas are assumed for this 
 method. When the data are given in the form of a network\, usually there i
 s only one network available. If the data are hypothesised to come from an
  exponential random graph model\, the likelihood cannot be calculated expl
 icitly. Using a Stein operator for these models we introduce a kernelized 
 goodness of fit test and illustrate its performance. \n \nThis talk is bas
 ed on joint work with Guillaume Mijoule\, Nathan Ross\, Yvik Swan and Wenk
 ai Xu.\n
LOCATION:https://researchseminars.org/talk/PVSeminar/5/
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