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June 2020 Bayesian Bootstraps for Massive Data
Andrés F. Barrientos, Víctor Peña
Bayesian Anal. 15(2): 363-388 (June 2020). DOI: 10.1214/19-BA1155


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.


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Andrés F. Barrientos. Víctor Peña. "Bayesian Bootstraps for Massive Data." Bayesian Anal. 15 (2) 363 - 388, June 2020.


Published: June 2020
First available in Project Euclid: 10 May 2019

MathSciNet: MR4078718
Digital Object Identifier: 10.1214/19-BA1155

Keywords: Bayesian nonparametric , big data , bootstrap , scalable inference


Vol.15 • No. 2 • June 2020
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