首页    期刊浏览 2024年12月03日 星期二
登录注册

文章基本信息

  • 标题:Feature Allocations, Probability Functions, and Paintboxes
  • 本地全文:下载
  • 作者:Tamara Broderick ; Jim Pitman ; Michael I. Jordan
  • 期刊名称:Bayesian Analysis
  • 印刷版ISSN:1931-6690
  • 电子版ISSN:1936-0975
  • 出版年度:2013
  • 卷号:8
  • 期号:4
  • 页码:801-836
  • DOI:10.1214/13-BA823
  • 语种:English
  • 出版社:International Society for Bayesian Analysis
  • 摘要:The problem of inferring a clustering of a data set has been the subject of much research in Bayesian analysis, and there currently exists a solid mathematical foundation for Bayesian approaches to clustering. In particular, the class of probability distributions over partitions of a data set has been characterized in a number of ways, including via exchangeable partition probability functions (EPPFs) and the Kingman paintbox. Here, we develop a generalization of the clustering problem, called feature allocation, where we allow each data point to belong to an arbitrary, non-negative integer number of groups, now called features or topics. We define and study an “exchangeable feature probability function” (EFPF)—analogous to the EPPF in the clustering setting—for certain types of feature models. Moreover, we introduce a “feature paintbox” characterization—analogous to the Kingman paintbox for clustering—of the class of exchangeable feature models. We provide a further characterization of the subclass of feature allocations that have EFPF representations.
国家哲学社会科学文献中心版权所有