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Approaches to statistical modeling of network data

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If you have a question about this talk, please contact Zoubin Ghahramani.

General topic of my work is modeling of dynamic network data. Two contributions will be presented. At first I will speak about concepts and symmetry considerations underlying the models for network data. I will present a functional representation theorem for Markov exchangeable sequences. Next, I will describe an extension of latent distance model of Hoff and coauthors (JASA, 2002) to the case of dynamic network where the evolution of latent positions is assumed to follow an infinite hidden Markov model.

This talk is part of the Machine Learning @ CUED series.

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