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PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:"Bayesian Nonparametric Modeling of Network Data"
DTSTART:20170119T150000
DTEND:20170119T160000
DTSTAMP:20260428T175448Z
UID:6d88871a932d6366bc0fb7f7a42eb77e39aa114715eae846bdbabf7c
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Daniele Durante  (Università di Padova) \nMany fields of
  research provide increasingly complex data along with novel motivating ap
 plications and new methodological questions. In approaching these data set
 s it is fundamental to rely on parsimonious representations which make the
  problem tractable and provide interpretable inference procedures to draw 
 meaningful conclusions. However\, in reducing complexity\, it is important
  to avoid restrictive models that lead to\ninadequate characterization of 
 relevant patterns underlying the observed data. Within this framework\, ne
 twork data representing relationship structures among a set of nodes are a
  relevant example. Although there has been abundant focus on models for a 
 single network\, there is a lack of methods for replicated network-valued 
 data monitored in different times or collected from a common population di
 stribution. These data open new\navenues for studying underlying connectiv
 ity patterns\, how they are distributed in the population and if this dist
 ribution changes in time or across predictors of interest. Motivated by ne
 uroscience and social science applications\, I will discuss some issues as
 sociated with available statistical models and I will outline recent metho
 ds I proposed to cover some of the current gaps via Bayesian nonparametric
  models leveraging latent space representations.\n 
LOCATION:CIB - BI A0 448 http://plan.epfl.ch/?room=BIA0448
STATUS:CONFIRMED
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