Bayesian nonparametric models for ranked data
  We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs sampler for posterior simulation. We develop a time-varying extension of our model, and apply it to the New York Times lists of weekly bestselling books. 
Francois Caron (INRIA Bordeaux - Sud-Ouest, IMB), Yee Whye Teh
stat.ML cs.LG stat.ME