<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">R. Thibaux</style></author><author><style face="normal" font="default" size="100%">M. Jordan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical beta processes and the Indian buffet process</style></title><secondary-title><style face="normal" font="default" size="100%">11th Conference on Artificial Intelligence and Statistics (AISTAT)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Puerto Rico</style></pub-location><abstract><style face="normal" font="default" size="100%">We show that the beta process is the de Finetti mixing distribution underlying the Indian buffet process of [2]. This result shows that the beta process plays the role for the Indian buffet process that the Dirichlet process plays for Chinese restaurant process, a parallel that guides us in deriving analogs for the beta process of the many known extensions of the Dirichlet process. In particular we define Bayesian hierarchies of beta processes and use the connection to the beta process to develop posterior inference algorithms for the Indian buffet process. We also present an application to document classification, exploring a relationship between the hierarchical beta process and smoothed naive Bayes models.</style></abstract></record></records></xml>