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SUMMARY:Discrete structures and prediction in Bayesian Nonparametrics - Ig
 or Pruenster (Universita' Bocconi)
DTSTART:20170310T160000Z
DTEND:20170310T170000Z
UID:TALK70613@talks.cam.ac.uk
CONTACT:Quentin Berthet
DESCRIPTION:Discrete random probability measures and the exchangeable rand
 om partitions they induce are key tools for addressing a variety of estima
 tion and prediction problems in Bayesian inference. In this talk we focus 
 on the family of Gibbs-type priors\, a recent elegant and intuitive genera
 lization of the Dirichlet and the Pitman-Yor process priors. Several distr
 ibutional properties are presented and their implications for Bayesian non
 parametric inference highlighted. Illustrations in the contexts of mixture
  modeling\, species sampling and curve estimation are provided.\n
LOCATION:MR12\, Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge.
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