CBMS 2010
Bayesian nonparametric (BNP) methods combine the advantages of Bayesian modeling (e.g., ability to incorporate prior information, full and exact inference, ready extensions to hierarchical settings) with the appeal of nonparametric inference. In particular, they provide data-driven, albeit model-based, inference and, importantly, more reliable predictions than parametric models.
Theoretical research on NPB methods and their applications has grown dramatically in the last fifteen years. This has produced a massive body of scattered literature, which can be daunting for newcomers and hard to follow even for specialists. For more information, visit the website.
Main Lecturer: Peter Muller (UT M.D. Anderson Cancer Center)
Invited Speakers: Michael Jordan (University of California, Berkeley), Peter Hoff (University of Washington) and Wesley Johnson (University of California, Irvine).
Local Organizers: Abel Rodriguez and Athanasios Kottas.