文章基本信息

标题：Beta-Binomial stick-breaking non-parametric prior
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作者：María F. Gil–Leyva ; Ramsés H. Mena ; Theodoros Nicoleris 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2020
卷号：14
期号：1
页码：1479-1507
DOI：10.1214/20-EJS1694
语种：English
出版社：Institute of Mathematical Statistics
摘要：A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete random probability measure arises. The chain’s dependence parameter controls the ordering of the stick-breaking weights, and thus tunes the model’s label-switching ability. Also, by tuning this parameter, the resulting class contains the Dirichlet process and the Geometric process priors as particular cases, which is of interest for MCMC implementations.
关键词：Beta-Binomial Markov chain; density estimation; Dirichlet process prior; geometric process prior; stick-breaking prior