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SUMMARY:"A General Framework for Uncovering Dependence Networks"
DTSTART:20170119T100000
DTEND:20170119T110000
DTSTAMP:20260407T111344Z
UID:4d5f0359380b7fc51b979b125b092df7958a5dbff354b9a15bbc7944
CATEGORIES:Conferences - Seminars
DESCRIPTION:Assistant Prof. Johannes Lederer (University of Washington)  W
 ebpage: www.johanneslederer.com\nDependencies in multivariate observations
  are a unique gateway to uncovering relationships among processes. An appr
 oach that has proved particularly successful in modeling and visualizing s
 uch dependence structures is the use of graphical models. However\, wherea
 s graphical models have been formulated for finite count data and Gaussian
 -type data\, many other data types prevalent in the sciences have not been
  accounted for. For example\, it is believed that insights into microbial 
 interactions in human habitats\, such as the gut or the oral cavity\, can 
 be deduced from analyzing the dependencies in microbial abundance data\, a
  data type that is not amenable to standard classes of graphical models. W
 e present a novel framework that unifies existing classes of graphical mod
 els and provides other classes that extend the concept of graphical models
  to a broad variety of discrete and continuous data\, both in low- and hig
 h-dimensional settings. Moreover\, we present a corresponding set of stati
 stical methods and theoretical guarantees that allows for efficient estima
 tion and inference in the framework.\n \n 
LOCATION:CIB - BI A0 448 http://plan.epfl.ch/?room=BIA0448
STATUS:CONFIRMED
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