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Dr. Johannes Lederer

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A General Framework for Uncovering Dependence Networks

  • FDM-Seminar
Wann 28.04.2017
von 12:00 bis 13:00
Wo Eckerstraße 1, Raum 404, 4. OG
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Dependencies in multivariate observations are a unique gateway to
uncovering relationships among processes. An approach that has proved
particularly successful in modeling and visualizing such dependence
structures is the use of graphical models. However, whereas 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. We present a novel framework that unifies existing classes of
graphical models and provides other classes that extend the concept of
graphical models to a broad variety of discrete and continuous data,
both in low- and high-dimensional settings. Moreover, we present a
corresponding set of statistical methods and theoretical guarantees that
allows for efficient estimation and inference in the framework.

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