Abstract Details
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Activity Number:
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61
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308814 |
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Title:
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Nonparametric Priors for Exchangeable Graphs and Arrays
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Author(s):
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Peter Orbanz*+
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Companies:
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Columbia University
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Keywords:
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Abstract:
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Suppose we observe data that aggregates into a graph, or more generally, a matrix or a higher-order array. As more data becomes available, the size of the graph increases. I will sketch the general form of nonparametric Bayesian models for such data in the case where the graph is exchangeable -- that is, where any two isomorphic graphs have the same probability of occurrence -- and illustrate the concept with a specific model. I will also discuss the validity of the exchangeability assumptions, and why exchangeable models are inherently misspecified if the graph represents a "network".
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Authors who are presenting talks have a * after their name.
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