2017/05/28 by Barrientos, Andrés F., Peña, Víctor
#Computation (stat.CO) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.1705.09998
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 (SDB; 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.