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Inference in the infinite relational model

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SNAW05 - Bayesian methods for networks

The infinite relational model and similar related non-parametric mixture models are very powerful for characterizing the structure in complex networks. But (approximate) inference can be challenging, especially for large networks, and both MCMC and variational inference is often hampered by local optima in the posterior distribution. These issues are discussed in the context of learning a high resolution parcellation of human brain structural connectivity network.

This talk is part of the Isaac Newton Institute Seminar Series series.

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