On the stick-breaking representation of normalized inverse Gaussian priors

• 成果类型: Article
• 署名作者: Favaro, S.; Lijoi, A.; Prunster, I.
• 署名单位: University of Turin; University of Pavia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass023
• 发表日期: 2012
• 页码: 663674
• 关键词: dirichlet process; distributions; models

摘要

Random probability measures are the main tool for Bayesian nonparametric inference, with their laws acting as prior distributions. Many well-known priors used in practice admit different, though equivalent, representations. In terms of computational convenience, stick-breaking representations stand out. In this paper we focus on the normalized inverse Gaussian process and provide a completely explicit stick-breaking representation for it. This result is of interest both from a theoretical viewpoint and for statistical practice.