Discrete structures and prediction in Bayesian Nonparametrics

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.

• Speaker: Igor Pruenster (Universita’ Bocconi)
• Friday 10 March 2017, 16:00–17:00
• Venue: MR12, Centre for Mathematical Sciences, Wilberforce Road, Cambridge..
• Series: Statistics; organiser: Quentin Berthet.