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Bayesian inference for exponential random graphs

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The exponential random graph is widely used in the statistical analysis of network data. It is a Markov random field model, and suffers from the problem that the likelihood is unavailable for all but trivially small networks. This talk will present two approaches; one simulation based, the other deterministic, which aim to overcome this difficulty.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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