Saturday, April 19, 2014

Patrick J. Wolfe: April 23rd

Nonparametric estimation of network structure

Networks are a key conceptual tool for analysis of rich data structures, yielding meaningful summaries in the biological as well as other sciences.  As datasets become larger, however, the interpretation of network-based summaries becomes more challenging.  A natural next step in this context is to think of modeling a network nonparametrically -- and here we will show how such an approach is possible, both in theory and in practice.  As with a histogram, nonparametric models can fully represent variation in a network, without presupposing a particular set of motifs or other distributional forms.  Advantages and limitations of the approach will be discussed, along with open problems at the methodological frontier of statistical network analysis.  Joint work with David Choi ( and Sofia Olhede (,

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