文章基本信息

标题：Bayesian Bootstraps for Massive Data
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作者：Andrés F. Barrientos ; Víctor Peña
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2020
卷号：15
期号：2
页码：363-388
DOI：10.1214/19-BA1155
语种：English
出版社：International Society for Bayesian Analysis
摘要：In this article, we present data-subsetting algorithms that allow for the approximate and scalable implementation of the Bayesian bootstrap. They are analogous to two existing algorithms in the frequentist literature: the bag of little bootstraps (Kleiner et al., 2014) and the subsampled double bootstrap (Sengupta et al., 2016). Our algorithms have appealing theoretical and computational properties that are comparable to those of their frequentist counterparts. Additionally, we provide a strategy for performing lossless inference for a class of functionals of the Bayesian bootstrap and briefly introduce extensions to the Dirichlet Process.
关键词：bootstrap; big data; Bayesian nonparametric; scalable inference