Generalized Ewens-Pitman model for Bayesian clustering

• 成果类型: Article
• 署名作者: Crane, Harry
• 署名单位: Rutgers University System; Rutgers University New Brunswick
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu052
• 发表日期: 2015
• 页码: 231238
• 关键词: product partition models; number; population; sample; trees

摘要

We propose a Bayesian method for clustering from discrete data structures that commonly arise in genetics and other applications. This method is equivariant with respect to relabelling units; unsampled units do not interfere with sampled data; and missing data do not hinder inference. Cluster inference using the posterior mode performs well on simulated and real datasets, and the posterior predictive distribution enables supervised learning based on a partial clustering of the sample.