Bayesian nonparametric estimators derived from conditional Gibbs structures

• 成果类型: Article
• 署名作者: Lijoi, Antonio; Prunster, Igor; Walker, Stephen G.
• 署名单位: University of Pavia; University of Turin; University of Kent
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/07-AAP495
• 发表日期: 2008
• 页码: 1519-1547
• 关键词: probability; frequencies

摘要

We consider discrete nonparametric priors which induce Gibbs-type exchangeable random partitions and investigate their posterior behavior in detail. In particular, we deduce conditi onal distributions and the corresponding Bayesian nonparametric estimators, which can be readily exploited for predicting various features of additional samples. The results provide useful tools for genomic applications where prediction of future outcomes is required.