University of Cambridge > Talks.cam > Statistics > Discrete structures and prediction in Bayesian Nonparametrics

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Discrete structures and prediction in Bayesian Nonparametrics

Download to your calendar using vCal

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.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Cambridge Technology & New Media Research Cluster Room 8-8, Faculty of Asian and Middle Eastern Studies Webinar & FW11, Computer Laboratory, William Gates Building.

Other talks

Tea and Coffee Break Afternoon Break Title TBC Title TBC Talk title TBC Title abstract tbc
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity