Inference in the infinite relational model

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• Mikkel Schmidt (Technical University of Denmark)
• Wednesday 27 July 2016, 14:30-15:00
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

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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