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Discrete structures and prediction in Bayesian Nonparametrics

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If you have a question about this talk, please contact Quentin Berthet.

Discrete random probability measures and the exchangeable random partitions they induce are key tools for addressing a variety of estimation and prediction problems in Bayesian inference. In this talk we focus on the family of Gibbs-type priors, a recent elegant and intuitive generalization of the Dirichlet and the Pitman-Yor process priors. Several distributional properties are presented and their implications for Bayesian nonparametric inference highlighted. Illustrations in the contexts of mixture modeling, species sampling and curve estimation are provided.

This talk is part of the Statistics series.

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